In [0]:
# catboost package installation
!pip install catboost
Requirement already satisfied: catboost in /usr/local/lib/python3.6/dist-packages (0.21)
Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.12.0)
Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from catboost) (0.10.1)
Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.17.5)
Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from catboost) (3.1.3)
Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from catboost) (1.4.1)
Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from catboost) (4.4.1)
Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (0.25.3)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (1.1.0)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.4.6)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (0.10.0)
Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.6.1)
Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->catboost) (1.3.3)
Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2018.9)
Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->catboost) (45.1.0)
In [0]:
# importing libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import time
from datetime import datetime
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import GridSearchCV
import lightgbm as lgb
from catboost import CatBoostClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from scipy import stats
import xgboost as xgb

# command for producing visualizations with higher resolution in notebook
plt.rcParams['figure.figsize'] = (13,8)
%config InlineBackend.figure_format = 'retina'
In [0]:
# Connecting colab to drive to import data files.
from google.colab import drive
drive.mount('/content/gdrive')
Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).

Part 1: Customer Segmentation Report

In [0]:
azdias = pd.read_csv('/content/gdrive/My Drive/Bertelsmann Data Science Project/Azdias.csv',dtype={18:'str',19:'str'})
customers = pd.read_csv('/content/gdrive/My Drive/Bertelsmann Data Science Project/Customers.csv',dtype={18:'str',19:'str'})
att_vals = pd.read_excel('/content/gdrive/My Drive/Bertelsmann Data Science Project/DIAS Attributes - Values 2017.xlsx')
In [0]:
del att_vals['Unnamed: 0']

Data pre-processing

In [0]:
# filling missing values of Unnamed: 1 with ffill.
att_vals['Unnamed: 1'].ffill(axis=0,inplace=True)
In [0]:
att_vals.columns = att_vals.iloc[0].values
att_vals.drop(index = 0,inplace=True)
In [0]:
features = att_vals['Attribute'].unique()
In [0]:
# since we only have feature info of limited columns therefore we will be dropping columns whose description is not mentioned.
coll = [col for col in azdias.columns if col not in features]
azdias.drop(columns=coll,inplace=True)
customers.drop(columns=coll,inplace=True)
In [0]:
# finding values using meaning column that represent missing values.
att_vals_na = att_vals[(att_vals['Meaning'].str.contains("unknown") | att_vals['Meaning'].str.contains("no "))]
pd.set_option('display.max_rows', 500)
att_vals_na
Out[0]:
Attribute Description Value Meaning
1 AGER_TYP best-ager typology -1 unknown
2 AGER_TYP NaN 0 no classification possible
6 ALTERSKATEGORIE_GROB age classification through prename analysis -1, 0 unknown
12 ALTER_HH main age within the household 0 unknown / no main age detectable
34 ANREDE_KZ gender -1, 0 unknown
41 BALLRAUM distance to next urban centre -1 unknown
49 BIP_FLAG business-flag indicating companies in the buil... -1 unknown
50 BIP_FLAG NaN 0 no company in the building
52 CAMEO_DEUG_2015 CAMEO classification 2015 - Uppergroup -1 unknown
106 CAMEO_DEUINTL_2015 CAMEO classification 2015 - international typo... -1 unknown
132 CJT_GESAMTTYP customer journey typology 0 unknown
139 D19_BANKEN_ANZ_12 transaction activity BANKS in the last 12 months 0 no transactions known
146 D19_BANKEN_ANZ_24 transaction activity BANKS in the last 24 months 0 no transactions known
162 D19_BANKEN_DATUM NaN 10 no transactions known
163 D19_BANKEN_DIREKT_RZ transactional activity based on the product gr... 0 no transaction known
171 D19_BANKEN_GROSS_RZ transactional activity based on the product gr... 0 no transaction known
179 D19_BANKEN_LOKAL_RZ transactional activity based on the product gr... 0 no transaction known
196 D19_BANKEN_OFFLINE_DATUM NaN 10 no transactions known
206 D19_BANKEN_ONLINE_DATUM NaN 10 no transactions known
207 D19_BANKEN_ONLINE_QUOTE_12 amount of online transactions within all trans... 0 no Online-transactions within the last 12 months
218 D19_BANKEN_REST_RZ transactional activity based on the product gr... 0 no transaction known
226 D19_BEKLEIDUNG_GEH_RZ transactional activity based on the product gr... 0 no transaction known
234 D19_BEKLEIDUNG_REST_RZ transactional activity based on the product gr... 0 no transaction known
242 D19_BILDUNG_RZ transactional activity based on the product gr... 0 no transaction known
250 D19_BIO_OEKO_RZ transactional activity based on the product gr... 0 no transaction known
258 D19_BUCH_RZ transactional activity based on the product gr... 0 no transaction known
266 D19_DIGIT_SERV_RZ transactional activity based on the product gr... 0 no transaction known
274 D19_DROGERIEARTIKEL_RZ transactional activity based on the product gr... 0 no transaction known
282 D19_ENERGIE_RZ transactional activity based on the product gr... 0 no transaction known
290 D19_FREIZEIT_RZ transactional activity based on the product gr... 0 no transaction known
298 D19_GARTEN_RZ transactional activity based on the product gr... 0 no transaction known
306 D19_GESAMT_ANZ_12 transaction activity TOTAL POOL in the last 12... 0 no transactions known
313 D19_GESAMT_ANZ_24 transaction activity TOTAL POOL in the last 24... 0 no transactions known
329 D19_GESAMT_DATUM NaN 10 no transactions known
339 D19_GESAMT_OFFLINE_DATUM NaN 10 no transactions known
349 D19_GESAMT_ONLINE_DATUM NaN 10 no transactions known
350 D19_GESAMT_ONLINE_QUOTE_12 amount of online transactions within all trans... 0 no Online-transactions within the last 12 months
361 D19_HANDWERK_RZ transactional activity based on the product gr... 0 no transaction known
369 D19_HAUS_DEKO_RZ transactional activity based on the product gr... 0 no transaction known
377 D19_KINDERARTIKEL_RZ transactional activity based on the product gr... 0 no transaction known
392 D19_KK_KUNDENTYP consumption movement in the last 12 months -1 unknown
399 D19_KOSMETIK_RZ transactional activity based on the product gr... 0 no transaction known
407 D19_LEBENSMITTEL_RZ transactional activity based on the product gr... 0 no transaction known
415 D19_LOTTO_RZ transactional activity based on the product gr... 0 no transaction known
423 D19_NAHRUNGSERGAENZUNG_RZ transactional activity based on the product gr... 0 no transaction known
431 D19_RATGEBER_RZ transactional activity based on the product gr... 0 no transaction known
439 D19_REISEN_RZ transactional activity based on the product gr... 0 no transaction known
447 D19_SAMMELARTIKEL_RZ transactional activity based on the product gr... 0 no transaction known
455 D19_SCHUHE_RZ transactional activity based on the product gr... 0 no transaction known
463 D19_SONSTIGE_RZ transactional activity based on the product gr... 0 no transaction known
471 D19_TECHNIK_RZ transactional activity based on the product gr... 0 no transaction known
479 D19_TELKO_ANZ_12 transaction activity TELCO in the last 12 months 0 no transactions known
486 D19_TELKO_ANZ_24 transaction activity TELCO in the last 24 months 0 no transactions known
502 D19_TELKO_DATUM NaN 10 no transactions known
503 D19_TELKO_MOBILE_RZ transactional activity based on the product gr... 0 no transaction known
520 D19_TELKO_OFFLINE_DATUM NaN 10 no transactions known
530 D19_TELKO_ONLINE_DATUM NaN 10 no transactions known
531 D19_TELKO_REST_RZ transactional activity based on the product gr... 0 no transaction known
539 D19_TIERARTIKEL_RZ transactional activity based on the product gr... 0 no transaction known
547 D19_VERSAND_ANZ_12 transaction activity MAIL-ORDER in the last 12... 0 no transactions known
554 D19_VERSAND_ANZ_24 transaction activity MAIL-ORDER in the last 24... 0 no transactions known
570 D19_VERSAND_DATUM NaN 10 no transactions known
580 D19_VERSAND_OFFLINE_DATUM NaN 10 no transactions known
590 D19_VERSAND_ONLINE_DATUM NaN 10 no transactions known
591 D19_VERSAND_ONLINE_QUOTE_12 amount of online transactions within all trans... 0 no Online-transactions within the last 12 months
602 D19_VERSAND_REST_RZ transactional activity based on the product gr... 0 no transaction known
610 D19_VERSICHERUNGEN_RZ transactional activity based on the product gr... 0 no transaction known
618 D19_VERSI_ANZ_12 transaction activity INSURANCE in the last 12 ... 0 no transactions known
625 D19_VERSI_ANZ_24 transaction activity INSURANCE in the last 24 ... 0 no transactions known
632 D19_VOLLSORTIMENT_RZ transactional activity based on the product gr... 0 no transaction known
640 D19_WEIN_FEINKOST_RZ transactional activity based on the product gr... 0 no transaction known
648 EWDICHTE density of inhabitants per square kilometer -1 unknown
655 FINANZTYP best descirbing financial type for the person -1 unknown
662 FINANZ_ANLEGER financial typology: investor -1 unknown
668 FINANZ_HAUSBAUER financial typology: main focus is the own house -1 unknown
674 FINANZ_MINIMALIST financial typology: low financial interest -1 unknown
680 FINANZ_SPARER financial typology: money saver -1 unknown
686 FINANZ_UNAUFFAELLIGER financial typology: unremarkable -1 unknown
692 FINANZ_VORSORGER financial typology: be prepared -1 unknown
698 GEBAEUDETYP type of building (residential or commercial) -1, 0 unknown
725 GEOSCORE_KLS7 microgeographical risk index concerning popula... -1, 0 unknown
726 GEOSCORE_KLS7 NaN 1 almost no risk
735 HAUSHALTSSTRUKTUR structure of the household (single-hh, couple ... -1, 0 unknown
745 HEALTH_TYP health typology -1 unknown
750 HH_EINKOMMEN_SCORE estimated household net income -1, 0 unknown
757 INNENSTADT distance to the city centre -1 unknown
766 KBA05_ALTER1 share of car owners less than 31 years old -1, 9 unknown
772 KBA05_ALTER2 share of car owners inbetween 31 and 45 years ... -1, 9 unknown
778 KBA05_ALTER3 share of car owners inbetween 45 and 60 years ... -1, 9 unknown
784 KBA05_ALTER4 share of cars owners elder than 61 years -1, 9 unknown
791 KBA05_ANHANG share of trailers in the microcell -1, 9 unknown
796 KBA05_ANTG1 number of 1-2 family houses in the cell -1 unknown
797 KBA05_ANTG1 NaN 0 no 1-2 family homes
802 KBA05_ANTG2 number of 3-5 family houses in the cell -1 unknown
803 KBA05_ANTG2 NaN 0 no 3-5 family homes
808 KBA05_ANTG3 number of 6-10 family houses in the cell -1 unknown
809 KBA05_ANTG3 NaN 0 no 6-10 family homes
813 KBA05_ANTG4 number of >10 family houses in the cell -1 unknown
814 KBA05_ANTG4 NaN 0 no >10 family homes
822 KBA05_AUTOQUOT NaN -1, 9 unknown
823 KBA05_BAUMAX most common building-type within the cell -1, 0 unknown
829 KBA05_CCM1 share of cars with less than 1399ccm -1, 9 unknown
835 KBA05_CCM2 share of cars with 1400ccm to 1799 ccm -1, 9 unknown
841 KBA05_CCM3 share of cars with 1800ccm to 2499 ccm -1, 9 unknown
847 KBA05_CCM4 share of cars with more than 2499ccm -1, 9 unknown
853 KBA05_DIESEL share of cars with Diesel-engine in the microcell -1, 9 unknown
859 KBA05_FRAU share of female car owners -1, 9 unknown
865 KBA05_GBZ number of buildings in the microcell -1, 0 unknown
871 KBA05_HERST1 share of top German manufacturer (Mercedes, BMW) -1, 9 unknown
878 KBA05_HERST2 share of Volkswagen-Cars (including Audi) -1, 9 unknown
885 KBA05_HERST3 share of Ford/Opel -1, 9 unknown
892 KBA05_HERST4 share of European manufacturer (e.g. Fiat, Peu... -1, 9 unknown
899 KBA05_HERST5 share of asian manufacturer (e.g. Toyota, Kia,... -1, 9 unknown
906 KBA05_HERSTTEMP development of the most common car manufacture... -1, 9 unknown
912 KBA05_KRSAQUOT share of cars per household (reffered to count... -1, 9 unknown
918 KBA05_KRSHERST1 share of Mercedes/BMW (reffered to the county ... -1, 9 unknown
924 KBA05_KRSHERST2 share of Volkswagen (reffered to the county av... -1, 9 unknown
930 KBA05_KRSHERST3 share of Ford/Opel (reffered to the county ave... -1, 9 unknown
936 KBA05_KRSKLEIN share of small cars (referred to the county av... -1, 9 unknown
940 KBA05_KRSOBER share of upper class cars (referred to the cou... -1, 9 unknown
944 KBA05_KRSVAN share of vans (referred to the county average) -1, 9 unknown
948 KBA05_KRSZUL share of newbuilt cars (referred to the county... -1, 9 unknown
952 KBA05_KW1 share of cars with less than 59 KW engine power -1, 9 unknown
958 KBA05_KW2 share of cars with an engine power between 60 ... -1, 9 unknown
964 KBA05_KW3 share of cars with an engine power of more tha... -1, 9 unknown
970 KBA05_MAXAH most common age of car owners in the microcell -1, 9 unknown
976 KBA05_MAXBJ most common age of the cars in the microcell -1, 9 unknown
981 KBA05_MAXHERST most common car manufacturer in the microcell -1, 9 unknown
987 KBA05_MAXSEG most common car segment in the microcell -1, 9 unknown
992 KBA05_MAXVORB most common preowner structure in the microcell -1, 9 unknown
993 KBA05_MAXVORB NaN 1 no preowner
996 KBA05_MOD1 share of upper class cars (in an AZ specific d... -1, 9 unknown
1002 KBA05_MOD2 share of middle class cars (in an AZ specific ... -1, 9 unknown
1008 KBA05_MOD3 share of Golf-class cars (in an AZ specific de... -1, 9 unknown
1014 KBA05_MOD4 share of small cars (in an AZ specific definit... -1, 9 unknown
1021 KBA05_MOD8 share of vans (in an AZ specific definition) -1, 9 unknown
1026 KBA05_MODTEMP development of the most common car segment in ... -1, 9 unknown
1032 KBA05_MOTOR most common engine size in the microcell -1, 9 unknown
1037 KBA05_MOTRAD share of motorcycles per household -1, 9 unknown
1042 KBA05_SEG1 share of very small cars (Ford Ka etc.) in the... -1, 9 unknown
1047 KBA05_SEG10 share of more specific cars (Vans, convertable... -1, 9 unknown
1053 KBA05_SEG2 share of small and very small cars (Ford Fiest... -1, 9 unknown
1059 KBA05_SEG3 share of lowe midclass cars (Ford Focus etc.) ... -1, 9 unknown
1065 KBA05_SEG4 share of middle class cars (Ford Mondeo etc.) ... -1, 9 unknown
1071 KBA05_SEG5 share of upper middle class cars and upper cla... -1, 9 unknown
1077 KBA05_SEG6 share of upper class cars (BMW 7er etc.) in th... -1, 9 unknown
1080 KBA05_SEG7 share of all-terrain vehicles and MUVs in the ... -1, 9 unknown
1085 KBA05_SEG8 share of roadster and convertables in the micr... -1, 9 unknown
1090 KBA05_SEG9 share of vans in the microcell -1, 9 unknown
1095 KBA05_VORB0 share of cars with no preowner -1, 9 unknown
1101 KBA05_VORB1 share of cars with one or two preowner -1, 9 unknown
1107 KBA05_VORB2 share of cars with more than two preowner -1, 9 unknown
1114 KBA05_ZUL1 share of cars built before 1994 -1, 9 unknown
1120 KBA05_ZUL2 share of cars built between 1994 and 2000 -1, 9 unknown
1126 KBA05_ZUL3 share of cars built between 2001 and 2002 -1, 9 unknown
1133 KBA05_ZUL4 share of cars built from 2003 on -1, 9 unknown
1140 KBA13_ALTERHALTER_30 share of car owners below 31 within the PLZ8 -1 unknown
1147 KBA13_ALTERHALTER_45 share of car owners between 31 and 45 within t... -1 unknown
1154 KBA13_ALTERHALTER_60 share of car owners between 46 and 60 within t... -1 unknown
1161 KBA13_ALTERHALTER_61 share of car owners elder than 61 within the PLZ8 -1 unknown
1169 KBA13_AUDI share of AUDI within the PLZ8 -1 unknown
1176 KBA13_AUTOQUOTE share of cars per household within the PLZ8 -1 unknown
1183 KBA13_BJ_1999 share of cars built between 1995 and 1999 with... -1 unknown
1190 KBA13_BJ_2000 share of cars built between 2000 and 2003 with... -1 unknown
1197 KBA13_BJ_2004 share of cars built before 2004 within the PLZ8 -1 unknown
1204 KBA13_BJ_2006 share of cars built between 2005 and 2006 with... -1 unknown
1211 KBA13_BJ_2008 share of cars built in 2008 within the PLZ8 -1 unknown
1218 KBA13_BJ_2009 share of cars built in 2009 within the PLZ8 -1 unknown
1225 KBA13_BMW share of BMW within the PLZ8 -1 unknown
1232 KBA13_CCM_1000 share of cars with less than 1000ccm within th... -1 unknown
1239 KBA13_CCM_1200 share of cars with 1000ccm to 1199ccm within t... -1 unknown
1246 KBA13_CCM_1400 share of cars with 1200ccm to 1399ccm within t... -1 unknown
1253 KBA13_CCM_0_1400 share of cars with less than 1400ccm within th... -1 unknown
1260 KBA13_CCM_1500 share of cars with 1400ccm to 1499ccm within t... -1 unknown
1267 KBA13_CCM_1400_2500 share of cars with 1400ccm to 2499ccm within t... -1 unknown
1274 KBA13_CCM_1600 share of cars with 1500ccm to 1599ccm within t... -1 unknown
1281 KBA13_CCM_1800 share of cars with 1600ccm to 1799ccm within t... -1 unknown
1288 KBA13_CCM_2000 share of cars with 1800ccm to 1999ccm within t... -1 unknown
1295 KBA13_CCM_2500 share of cars with 2000ccm to 2499ccm within t... -1 unknown
1302 KBA13_CCM_2501 share of cars with more than 2500ccm within th... -1 unknown
1309 KBA13_CCM_3000 share of cars with 2500ccm to 2999ccm within t... -1 unknown
1316 KBA13_CCM_3001 share of cars with more than 3000ccm within th... -1 unknown
1323 KBA13_FAB_ASIEN share of other Asian Manufacturers within the ... -1 unknown
1330 KBA13_FAB_SONSTIGE share of other Manufacturers within the PLZ8 -1 unknown
1337 KBA13_FIAT share of FIAT within the PLZ8 -1 unknown
1344 KBA13_FORD share of FORD within the PLZ8 -1 unknown
1351 KBA13_HALTER_20 share of car owners below 21 within the PLZ8 -1 unknown
1358 KBA13_HALTER_25 share of car owners between 21 and 25 within t... -1 unknown
1365 KBA13_HALTER_30 share of car owners between 26 and 30 within t... -1 unknown
1372 KBA13_HALTER_35 share of car owners between 31 and 35 within t... -1 unknown
1379 KBA13_HALTER_40 share of car owners between 36 and 40 within t... -1 unknown
1386 KBA13_HALTER_45 share of car owners between 41 and 45 within t... -1 unknown
1393 KBA13_HALTER_50 share of car owners between 46 and 50 within t... -1 unknown
1400 KBA13_HALTER_55 share of car owners between 51 and 55 within t... -1 unknown
1407 KBA13_HALTER_60 share of car owners between 56 and 60 within t... -1 unknown
1414 KBA13_HALTER_65 share of car owners between 61 and 65 within t... -1 unknown
1421 KBA13_HALTER_66 share of car owners over 66 within the PLZ8 -1 unknown
1428 KBA13_HERST_ASIEN share of Asian Manufacturers within the PLZ8 -1 unknown
1435 KBA13_HERST_AUDI_VW share of Volkswagen & Audi within the PLZ8 -1 unknown
1442 KBA13_HERST_BMW_BENZ share of BMW & Mercedes Benz within the PLZ8 -1 unknown
1449 KBA13_HERST_EUROPA share of European cars within the PLZ8 -1 unknown
1456 KBA13_HERST_FORD_OPEL share of Ford & Opel/Vauxhall within the PLZ8 -1 unknown
1463 KBA13_HERST_SONST share of other cars within the PLZ8 -1 unknown
1470 KBA13_KMH_110 share of cars with max speed 110 km/h within t... -1 unknown
1477 KBA13_KMH_140 share of cars with max speed between 110 km/h ... -1 unknown
1484 KBA13_KMH_180 share of cars with max speed between 110 km/h ... -1 unknown
1491 KBA13_KMH_0_140 share of cars with max speed 140 km/h within t... -1 unknown
1498 KBA13_KMH_140_210 share of cars with max speed between 140 and 2... -1 unknown
1505 KBA13_KMH_211 share of cars with a greater max speed than 21... -1 unknown
1512 KBA13_KMH_250 share of cars with max speed between 210 and 2... -1 unknown
1519 KBA13_KMH_251 share of cars with a greater max speed than 25... -1 unknown
1526 KBA13_KRSAQUOT share of cars per household (referred to the c... -1 unknown
1533 KBA13_KRSHERST_AUDI_VW share of Volkswagen (referred to the county av... -1 unknown
1540 KBA13_KRSHERST_BMW_BENZ share of BMW/Mercedes Benz (referred to the co... -1 unknown
1547 KBA13_KRSHERST_FORD_OPEL share of FORD/Opel (referred to the county ave... -1 unknown
1554 KBA13_KRSSEG_KLEIN share of small cars (referred to the county av... -1 unknown
1559 KBA13_KRSSEG_OBER share of upper class cars (referred to the cou... -1 unknown
1564 KBA13_KRSSEG_VAN share of vans (referred to the county average)... -1 unknown
1569 KBA13_KRSZUL_NEU share of newbuilt cars (referred to the county... -1 unknown
1574 KBA13_KW_30 share of cars up to 30 KW engine power - PLZ8 -1 unknown
1581 KBA13_KW_40 share of cars with an engine power between 31 ... -1 unknown
1588 KBA13_KW_50 share of cars with an engine power between 41 ... -1 unknown
1595 KBA13_KW_60 share of cars with an engine power between 51 ... -1 unknown
1602 KBA13_KW_0_60 share of cars up to 60 KW engine power - PLZ8 -1 unknown
1609 KBA13_KW_70 share of cars with an engine power between 61 ... -1 unknown
1616 KBA13_KW_61_120 share of cars with an engine power between 61 ... -1 unknown
1623 KBA13_KW_80 share of cars with an engine power between 71 ... -1 unknown
1630 KBA13_KW_90 share of cars with an engine power between 81 ... -1 unknown
1637 KBA13_KW_110 share of cars with an engine power between 91 ... -1 unknown
1644 KBA13_KW_120 share of cars with an engine power between 111... -1 unknown
1651 KBA13_KW_121 share of cars with an engine power more than 1... -1 unknown
1658 KBA13_MAZDA share of MAZDA within the PLZ8 -1 unknown
1665 KBA13_MERCEDES share of MERCEDES within the PLZ8 -1 unknown
1672 KBA13_MOTOR most common motor size within the PLZ8 -1 unknown
1678 KBA13_NISSAN share of NISSAN within the PLZ8 -1 unknown
1685 KBA13_OPEL share of OPEL within the PLZ8 -1 unknown
1692 KBA13_PEUGEOT share of PEUGEOT within the PLZ8 -1 unknown
1699 KBA13_RENAULT share of RENAULT within the PLZ8 -1 unknown
1706 KBA13_SEG_GELAENDEWAGEN share of allterrain within the PLZ8 -1 unknown
1713 KBA13_SEG_GROSSRAUMVANS share of big sized vans within the PLZ8 -1 unknown
1720 KBA13_SEG_KLEINST share of very small cars (Ford Ka etc.) in the... -1 unknown
1727 KBA13_SEG_KLEINWAGEN share of small and very small cars (Ford Fiest... -1 unknown
1734 KBA13_SEG_KOMPAKTKLASSE share of lowe midclass cars (Ford Focus etc.) ... -1 unknown
1741 KBA13_SEG_MINIVANS share of minivans within the PLZ8 -1 unknown
1748 KBA13_SEG_MINIWAGEN share of minicars within the PLZ8 -1 unknown
1755 KBA13_SEG_MITTELKLASSE share of middle class cars (Ford Mondeo etc.) ... -1 unknown
1762 KBA13_SEG_OBEREMITTELKLASSE share of upper middle class cars and upper cla... -1 unknown
1769 KBA13_SEG_OBERKLASSE share of upper class cars (BMW 7er etc.) in th... -1 unknown
1776 KBA13_SEG_SONSTIGE share of other cars within the PLZ8 -1 unknown
1783 KBA13_SEG_SPORTWAGEN share of sportscars within the PLZ8 -1 unknown
1790 KBA13_SEG_UTILITIES share of MUVs/SUVs within the PLZ8 -1 unknown
1797 KBA13_SEG_VAN share of vans within the PLZ8 -1 unknown
1804 KBA13_SEG_WOHNMOBILE share of roadmobiles within the PLZ8 -1 unknown
1811 KBA13_SITZE_4 number of cars with less than 5 seats in the PLZ8 -1 unknown
1818 KBA13_SITZE_5 number of cars with 5 seats in the PLZ8 -1 unknown
1825 KBA13_SITZE_6 number of cars with more than 5 seats in the PLZ8 -1 unknown
1832 KBA13_TOYOTA share of TOYOTA within the PLZ8 -1 unknown
1839 KBA13_VORB_0 share of cars with no preowner - PLZ8 -1 unknown
1846 KBA13_VORB_1 share of cars with 1 preowner - PLZ8 -1 unknown
1853 KBA13_VORB_1_2 share of cars with 1 or 2 preowner - PLZ8 -1 unknown
1860 KBA13_VORB_2 share of cars with 2 preowner - PLZ8 -1 unknown
1867 KBA13_VORB_3 share of cars with 3 or more preowner - PLZ8 -1 unknown
1874 KBA13_VW share of VOLKSWAGEN within the PLZ8 -1 unknown
1881 KKK purchasing power -1, 0 unknown
1994 NATIONALITAET_KZ nationaltity (scored by prename analysis) -1, 0 unknown
2004 ORTSGR_KLS9 size of the community -1 unknown
2014 OST_WEST_KZ flag indicating the former GDR/FRG -1 unknown
2017 PLZ8_ANTG1 number of 1-2 family houses in the PLZ8 -1 unknown
2023 PLZ8_ANTG2 number of 3-5 family houses in the PLZ8 -1 unknown
2029 PLZ8_ANTG3 number of 6-10 family houses in the PLZ8 -1 unknown
2034 PLZ8_ANTG4 number of >10 family houses in the PLZ8 -1 unknown
2043 PLZ8_GBZ number of buildings within the PLZ8 -1 unknown
2049 PLZ8_HHZ number of households within the PLZ8 -1 unknown
2055 PRAEGENDE_JUGENDJAHRE dominating movement in the person's youth (ava... -1, 0 unknown
2071 REGIOTYP neighbourhood -1, 0 unknown
2084 RELAT_AB NaN -1, 9 unknown
2085 RETOURTYP_BK_S return type 0 unknown
2091 SEMIO_DOM affinity indicating in what way the person is ... -1, 9 unknown
2099 SEMIO_ERL affinity indicating in what way the person is ... -1, 9 unknown
2107 SEMIO_FAM affinity indicating in what way the person is ... -1, 9 unknown
2115 SEMIO_KAEM affinity indicating in what way the person is ... -1, 9 unknown
2123 SEMIO_KRIT affinity indicating in what way the person is ... -1, 9 unknown
2131 SEMIO_KULT affinity indicating in what way the person is ... -1, 9 unknown
2139 SEMIO_LUST affinity indicating in what way the person is ... -1, 9 unknown
2147 SEMIO_MAT affinity indicating in what way the person is ... -1, 9 unknown
2155 SEMIO_PFLICHT affinity indicating in what way the person is ... -1, 9 unknown
2163 SEMIO_RAT affinity indicating in what way the person is ... -1, 9 unknown
2171 SEMIO_REL affinity indicating in what way the person is ... -1, 9 unknown
2179 SEMIO_SOZ affinity indicating in what way the person is ... -1, 9 unknown
2187 SEMIO_TRADV affinity indicating in what way the person is ... -1, 9 unknown
2195 SEMIO_VERT affinity indicating in what way the person is ... -1, 9 unknown
2203 SHOPPER_TYP shopping typology -1 unknown
2208 SOHO_FLAG small office/home office flag -1 unknown
2209 SOHO_FLAG NaN 0 no small office/home office
2211 TITEL_KZ flag whether this person holds an academic title -1, 0 unknown
2217 VERS_TYP insurance typology -1 unknown
2220 WOHNDAUER_2008 length of residence -1, 0 unknown
2230 WOHNLAGE residential-area -1 unknown
2231 WOHNLAGE NaN 0 no score calculated
2239 WACHSTUMSGEBIET_NB growing area (population growth in the last 5 ... -1, 0 unknown
2245 W_KEIT_KIND_HH likelihood of a child present in this household -1, 0 unknown
2252 ZABEOTYP typification of energy consumers -1, 9 unknown
In [0]:
nans = []
for attributes in att_vals_na['Attribute'].unique():
    _ = att_vals_na.loc[att_vals_na['Attribute'] == attributes, 'Value'].astype(str).str.cat(sep=',')
    _ = _.split(',')
    nans.append(_)
In [0]:
nans = pd.concat([pd.Series(att_vals_na['Attribute'].unique()), pd.Series(nans)], axis=1)
nans.columns = ['Attributes', 'missing_or_unknown']
nans
Out[0]:
Attributes missing_or_unknown
0 AGER_TYP [-1, 0]
1 ALTERSKATEGORIE_GROB [-1, 0]
2 ALTER_HH [0]
3 ANREDE_KZ [-1, 0]
4 BALLRAUM [-1]
5 BIP_FLAG [-1, 0]
6 CAMEO_DEUG_2015 [-1]
7 CAMEO_DEUINTL_2015 [-1]
8 CJT_GESAMTTYP [0]
9 D19_BANKEN_ANZ_12 [0]
10 D19_BANKEN_ANZ_24 [0]
11 D19_BANKEN_DATUM [10]
12 D19_BANKEN_DIREKT_RZ [0]
13 D19_BANKEN_GROSS_RZ [0]
14 D19_BANKEN_LOKAL_RZ [0]
15 D19_BANKEN_OFFLINE_DATUM [10]
16 D19_BANKEN_ONLINE_DATUM [10]
17 D19_BANKEN_ONLINE_QUOTE_12 [0]
18 D19_BANKEN_REST_RZ [0]
19 D19_BEKLEIDUNG_GEH_RZ [0]
20 D19_BEKLEIDUNG_REST_RZ [0]
21 D19_BILDUNG_RZ [0]
22 D19_BIO_OEKO_RZ [0]
23 D19_BUCH_RZ [0]
24 D19_DIGIT_SERV_RZ [0]
25 D19_DROGERIEARTIKEL_RZ [0]
26 D19_ENERGIE_RZ [0]
27 D19_FREIZEIT_RZ [0]
28 D19_GARTEN_RZ [0]
29 D19_GESAMT_ANZ_12 [0]
30 D19_GESAMT_ANZ_24 [0]
31 D19_GESAMT_DATUM [10]
32 D19_GESAMT_OFFLINE_DATUM [10]
33 D19_GESAMT_ONLINE_DATUM [10]
34 D19_GESAMT_ONLINE_QUOTE_12 [0]
35 D19_HANDWERK_RZ [0]
36 D19_HAUS_DEKO_RZ [0]
37 D19_KINDERARTIKEL_RZ [0]
38 D19_KK_KUNDENTYP [-1]
39 D19_KOSMETIK_RZ [0]
40 D19_LEBENSMITTEL_RZ [0]
41 D19_LOTTO_RZ [0]
42 D19_NAHRUNGSERGAENZUNG_RZ [0]
43 D19_RATGEBER_RZ [0]
44 D19_REISEN_RZ [0]
45 D19_SAMMELARTIKEL_RZ [0]
46 D19_SCHUHE_RZ [0]
47 D19_SONSTIGE_RZ [0]
48 D19_TECHNIK_RZ [0]
49 D19_TELKO_ANZ_12 [0]
50 D19_TELKO_ANZ_24 [0]
51 D19_TELKO_DATUM [10]
52 D19_TELKO_MOBILE_RZ [0]
53 D19_TELKO_OFFLINE_DATUM [10]
54 D19_TELKO_ONLINE_DATUM [10]
55 D19_TELKO_REST_RZ [0]
56 D19_TIERARTIKEL_RZ [0]
57 D19_VERSAND_ANZ_12 [0]
58 D19_VERSAND_ANZ_24 [0]
59 D19_VERSAND_DATUM [10]
60 D19_VERSAND_OFFLINE_DATUM [10]
61 D19_VERSAND_ONLINE_DATUM [10]
62 D19_VERSAND_ONLINE_QUOTE_12 [0]
63 D19_VERSAND_REST_RZ [0]
64 D19_VERSICHERUNGEN_RZ [0]
65 D19_VERSI_ANZ_12 [0]
66 D19_VERSI_ANZ_24 [0]
67 D19_VOLLSORTIMENT_RZ [0]
68 D19_WEIN_FEINKOST_RZ [0]
69 EWDICHTE [-1]
70 FINANZTYP [-1]
71 FINANZ_ANLEGER [-1]
72 FINANZ_HAUSBAUER [-1]
73 FINANZ_MINIMALIST [-1]
74 FINANZ_SPARER [-1]
75 FINANZ_UNAUFFAELLIGER [-1]
76 FINANZ_VORSORGER [-1]
77 GEBAEUDETYP [-1, 0]
78 GEOSCORE_KLS7 [-1, 0, 1]
79 HAUSHALTSSTRUKTUR [-1, 0]
80 HEALTH_TYP [-1]
81 HH_EINKOMMEN_SCORE [-1, 0]
82 INNENSTADT [-1]
83 KBA05_ALTER1 [-1, 9]
84 KBA05_ALTER2 [-1, 9]
85 KBA05_ALTER3 [-1, 9]
86 KBA05_ALTER4 [-1, 9]
87 KBA05_ANHANG [-1, 9]
88 KBA05_ANTG1 [-1, 0]
89 KBA05_ANTG2 [-1, 0]
90 KBA05_ANTG3 [-1, 0]
91 KBA05_ANTG4 [-1, 0]
92 KBA05_AUTOQUOT [-1, 9]
93 KBA05_BAUMAX [-1, 0]
94 KBA05_CCM1 [-1, 9]
95 KBA05_CCM2 [-1, 9]
96 KBA05_CCM3 [-1, 9]
97 KBA05_CCM4 [-1, 9]
98 KBA05_DIESEL [-1, 9]
99 KBA05_FRAU [-1, 9]
100 KBA05_GBZ [-1, 0]
101 KBA05_HERST1 [-1, 9]
102 KBA05_HERST2 [-1, 9]
103 KBA05_HERST3 [-1, 9]
104 KBA05_HERST4 [-1, 9]
105 KBA05_HERST5 [-1, 9]
106 KBA05_HERSTTEMP [-1, 9]
107 KBA05_KRSAQUOT [-1, 9]
108 KBA05_KRSHERST1 [-1, 9]
109 KBA05_KRSHERST2 [-1, 9]
110 KBA05_KRSHERST3 [-1, 9]
111 KBA05_KRSKLEIN [-1, 9]
112 KBA05_KRSOBER [-1, 9]
113 KBA05_KRSVAN [-1, 9]
114 KBA05_KRSZUL [-1, 9]
115 KBA05_KW1 [-1, 9]
116 KBA05_KW2 [-1, 9]
117 KBA05_KW3 [-1, 9]
118 KBA05_MAXAH [-1, 9]
119 KBA05_MAXBJ [-1, 9]
120 KBA05_MAXHERST [-1, 9]
121 KBA05_MAXSEG [-1, 9]
122 KBA05_MAXVORB [-1, 9, 1]
123 KBA05_MOD1 [-1, 9]
124 KBA05_MOD2 [-1, 9]
125 KBA05_MOD3 [-1, 9]
126 KBA05_MOD4 [-1, 9]
127 KBA05_MOD8 [-1, 9]
128 KBA05_MODTEMP [-1, 9]
129 KBA05_MOTOR [-1, 9]
130 KBA05_MOTRAD [-1, 9]
131 KBA05_SEG1 [-1, 9]
132 KBA05_SEG10 [-1, 9]
133 KBA05_SEG2 [-1, 9]
134 KBA05_SEG3 [-1, 9]
135 KBA05_SEG4 [-1, 9]
136 KBA05_SEG5 [-1, 9]
137 KBA05_SEG6 [-1, 9]
138 KBA05_SEG7 [-1, 9]
139 KBA05_SEG8 [-1, 9]
140 KBA05_SEG9 [-1, 9]
141 KBA05_VORB0 [-1, 9]
142 KBA05_VORB1 [-1, 9]
143 KBA05_VORB2 [-1, 9]
144 KBA05_ZUL1 [-1, 9]
145 KBA05_ZUL2 [-1, 9]
146 KBA05_ZUL3 [-1, 9]
147 KBA05_ZUL4 [-1, 9]
148 KBA13_ALTERHALTER_30 [-1]
149 KBA13_ALTERHALTER_45 [-1]
150 KBA13_ALTERHALTER_60 [-1]
151 KBA13_ALTERHALTER_61 [-1]
152 KBA13_AUDI [-1]
153 KBA13_AUTOQUOTE [-1]
154 KBA13_BJ_1999 [-1]
155 KBA13_BJ_2000 [-1]
156 KBA13_BJ_2004 [-1]
157 KBA13_BJ_2006 [-1]
158 KBA13_BJ_2008 [-1]
159 KBA13_BJ_2009 [-1]
160 KBA13_BMW [-1]
161 KBA13_CCM_1000 [-1]
162 KBA13_CCM_1200 [-1]
163 KBA13_CCM_1400 [-1]
164 KBA13_CCM_0_1400 [-1]
165 KBA13_CCM_1500 [-1]
166 KBA13_CCM_1400_2500 [-1]
167 KBA13_CCM_1600 [-1]
168 KBA13_CCM_1800 [-1]
169 KBA13_CCM_2000 [-1]
170 KBA13_CCM_2500 [-1]
171 KBA13_CCM_2501 [-1]
172 KBA13_CCM_3000 [-1]
173 KBA13_CCM_3001 [-1]
174 KBA13_FAB_ASIEN [-1]
175 KBA13_FAB_SONSTIGE [-1]
176 KBA13_FIAT [-1]
177 KBA13_FORD [-1]
178 KBA13_HALTER_20 [-1]
179 KBA13_HALTER_25 [-1]
180 KBA13_HALTER_30 [-1]
181 KBA13_HALTER_35 [-1]
182 KBA13_HALTER_40 [-1]
183 KBA13_HALTER_45 [-1]
184 KBA13_HALTER_50 [-1]
185 KBA13_HALTER_55 [-1]
186 KBA13_HALTER_60 [-1]
187 KBA13_HALTER_65 [-1]
188 KBA13_HALTER_66 [-1]
189 KBA13_HERST_ASIEN [-1]
190 KBA13_HERST_AUDI_VW [-1]
191 KBA13_HERST_BMW_BENZ [-1]
192 KBA13_HERST_EUROPA [-1]
193 KBA13_HERST_FORD_OPEL [-1]
194 KBA13_HERST_SONST [-1]
195 KBA13_KMH_110 [-1]
196 KBA13_KMH_140 [-1]
197 KBA13_KMH_180 [-1]
198 KBA13_KMH_0_140 [-1]
199 KBA13_KMH_140_210 [-1]
200 KBA13_KMH_211 [-1]
201 KBA13_KMH_250 [-1]
202 KBA13_KMH_251 [-1]
203 KBA13_KRSAQUOT [-1]
204 KBA13_KRSHERST_AUDI_VW [-1]
205 KBA13_KRSHERST_BMW_BENZ [-1]
206 KBA13_KRSHERST_FORD_OPEL [-1]
207 KBA13_KRSSEG_KLEIN [-1]
208 KBA13_KRSSEG_OBER [-1]
209 KBA13_KRSSEG_VAN [-1]
210 KBA13_KRSZUL_NEU [-1]
211 KBA13_KW_30 [-1]
212 KBA13_KW_40 [-1]
213 KBA13_KW_50 [-1]
214 KBA13_KW_60 [-1]
215 KBA13_KW_0_60 [-1]
216 KBA13_KW_70 [-1]
217 KBA13_KW_61_120 [-1]
218 KBA13_KW_80 [-1]
219 KBA13_KW_90 [-1]
220 KBA13_KW_110 [-1]
221 KBA13_KW_120 [-1]
222 KBA13_KW_121 [-1]
223 KBA13_MAZDA [-1]
224 KBA13_MERCEDES [-1]
225 KBA13_MOTOR [-1]
226 KBA13_NISSAN [-1]
227 KBA13_OPEL [-1]
228 KBA13_PEUGEOT [-1]
229 KBA13_RENAULT [-1]
230 KBA13_SEG_GELAENDEWAGEN [-1]
231 KBA13_SEG_GROSSRAUMVANS [-1]
232 KBA13_SEG_KLEINST [-1]
233 KBA13_SEG_KLEINWAGEN [-1]
234 KBA13_SEG_KOMPAKTKLASSE [-1]
235 KBA13_SEG_MINIVANS [-1]
236 KBA13_SEG_MINIWAGEN [-1]
237 KBA13_SEG_MITTELKLASSE [-1]
238 KBA13_SEG_OBEREMITTELKLASSE [-1]
239 KBA13_SEG_OBERKLASSE [-1]
240 KBA13_SEG_SONSTIGE [-1]
241 KBA13_SEG_SPORTWAGEN [-1]
242 KBA13_SEG_UTILITIES [-1]
243 KBA13_SEG_VAN [-1]
244 KBA13_SEG_WOHNMOBILE [-1]
245 KBA13_SITZE_4 [-1]
246 KBA13_SITZE_5 [-1]
247 KBA13_SITZE_6 [-1]
248 KBA13_TOYOTA [-1]
249 KBA13_VORB_0 [-1]
250 KBA13_VORB_1 [-1]
251 KBA13_VORB_1_2 [-1]
252 KBA13_VORB_2 [-1]
253 KBA13_VORB_3 [-1]
254 KBA13_VW [-1]
255 KKK [-1, 0]
256 NATIONALITAET_KZ [-1, 0]
257 ORTSGR_KLS9 [-1]
258 OST_WEST_KZ [-1]
259 PLZ8_ANTG1 [-1]
260 PLZ8_ANTG2 [-1]
261 PLZ8_ANTG3 [-1]
262 PLZ8_ANTG4 [-1]
263 PLZ8_GBZ [-1]
264 PLZ8_HHZ [-1]
265 PRAEGENDE_JUGENDJAHRE [-1, 0]
266 REGIOTYP [-1, 0]
267 RELAT_AB [-1, 9]
268 RETOURTYP_BK_S [0]
269 SEMIO_DOM [-1, 9]
270 SEMIO_ERL [-1, 9]
271 SEMIO_FAM [-1, 9]
272 SEMIO_KAEM [-1, 9]
273 SEMIO_KRIT [-1, 9]
274 SEMIO_KULT [-1, 9]
275 SEMIO_LUST [-1, 9]
276 SEMIO_MAT [-1, 9]
277 SEMIO_PFLICHT [-1, 9]
278 SEMIO_RAT [-1, 9]
279 SEMIO_REL [-1, 9]
280 SEMIO_SOZ [-1, 9]
281 SEMIO_TRADV [-1, 9]
282 SEMIO_VERT [-1, 9]
283 SHOPPER_TYP [-1]
284 SOHO_FLAG [-1, 0]
285 TITEL_KZ [-1, 0]
286 VERS_TYP [-1]
287 WOHNDAUER_2008 [-1, 0]
288 WOHNLAGE [-1, 0]
289 WACHSTUMSGEBIET_NB [-1, 0]
290 W_KEIT_KIND_HH [-1, 0]
291 ZABEOTYP [-1, 9]
In [0]:
# re-encoding values that represent missing as np.NAN
for row in nans['Attributes']:
    print(row)
    if row in azdias.columns:
        _m = nans.loc[nans['Attributes'] == row, 'missing_or_unknown'].iloc[0]
        _i = azdias.loc[:, row].isin(_m)
        azdias.loc[_i, row] = np.NaN
    else:
        continue

for row in nans['Attributes']:
    if row in customers.columns:
        _m = nans.loc[nans['Attributes'] == row, 'missing_or_unknown'].iloc[0]
        _i = customers.loc[:, row].isin(_m)
        customers.loc[_i, row] = np.NaN
    else:
        continue
AGER_TYP
ALTERSKATEGORIE_GROB
ALTER_HH
ANREDE_KZ
BALLRAUM
BIP_FLAG
CAMEO_DEUG_2015
CAMEO_DEUINTL_2015
CJT_GESAMTTYP
D19_BANKEN_ANZ_12
D19_BANKEN_ANZ_24
D19_BANKEN_DATUM
D19_BANKEN_DIREKT_RZ
D19_BANKEN_GROSS_RZ
D19_BANKEN_LOKAL_RZ
D19_BANKEN_OFFLINE_DATUM
D19_BANKEN_ONLINE_DATUM
D19_BANKEN_ONLINE_QUOTE_12
D19_BANKEN_REST_RZ
D19_BEKLEIDUNG_GEH_RZ
D19_BEKLEIDUNG_REST_RZ
D19_BILDUNG_RZ
D19_BIO_OEKO_RZ
D19_BUCH_RZ
D19_DIGIT_SERV_RZ
D19_DROGERIEARTIKEL_RZ
D19_ENERGIE_RZ
D19_FREIZEIT_RZ
D19_GARTEN_RZ
D19_GESAMT_ANZ_12
D19_GESAMT_ANZ_24
D19_GESAMT_DATUM
D19_GESAMT_OFFLINE_DATUM
D19_GESAMT_ONLINE_DATUM
D19_GESAMT_ONLINE_QUOTE_12
D19_HANDWERK_RZ
D19_HAUS_DEKO_RZ
D19_KINDERARTIKEL_RZ
D19_KK_KUNDENTYP
D19_KOSMETIK_RZ
D19_LEBENSMITTEL_RZ
D19_LOTTO_RZ
D19_NAHRUNGSERGAENZUNG_RZ
D19_RATGEBER_RZ
D19_REISEN_RZ
D19_SAMMELARTIKEL_RZ
D19_SCHUHE_RZ
D19_SONSTIGE_RZ
D19_TECHNIK_RZ
D19_TELKO_ANZ_12
D19_TELKO_ANZ_24
D19_TELKO_DATUM
D19_TELKO_MOBILE_RZ
D19_TELKO_OFFLINE_DATUM
D19_TELKO_ONLINE_DATUM
D19_TELKO_REST_RZ
D19_TIERARTIKEL_RZ
D19_VERSAND_ANZ_12
D19_VERSAND_ANZ_24
D19_VERSAND_DATUM
D19_VERSAND_OFFLINE_DATUM
D19_VERSAND_ONLINE_DATUM
D19_VERSAND_ONLINE_QUOTE_12
D19_VERSAND_REST_RZ
D19_VERSICHERUNGEN_RZ
D19_VERSI_ANZ_12
D19_VERSI_ANZ_24
D19_VOLLSORTIMENT_RZ
D19_WEIN_FEINKOST_RZ
EWDICHTE
FINANZTYP
FINANZ_ANLEGER
FINANZ_HAUSBAUER
FINANZ_MINIMALIST
FINANZ_SPARER
FINANZ_UNAUFFAELLIGER
FINANZ_VORSORGER
GEBAEUDETYP
GEOSCORE_KLS7
HAUSHALTSSTRUKTUR
HEALTH_TYP
HH_EINKOMMEN_SCORE
INNENSTADT
KBA05_ALTER1
KBA05_ALTER2
KBA05_ALTER3
KBA05_ALTER4
KBA05_ANHANG
KBA05_ANTG1
KBA05_ANTG2
KBA05_ANTG3
KBA05_ANTG4
KBA05_AUTOQUOT
KBA05_BAUMAX
KBA05_CCM1
KBA05_CCM2
KBA05_CCM3
KBA05_CCM4
KBA05_DIESEL
KBA05_FRAU
KBA05_GBZ
KBA05_HERST1
KBA05_HERST2
KBA05_HERST3
KBA05_HERST4
KBA05_HERST5
KBA05_HERSTTEMP
KBA05_KRSAQUOT
KBA05_KRSHERST1
KBA05_KRSHERST2
KBA05_KRSHERST3
KBA05_KRSKLEIN
KBA05_KRSOBER
KBA05_KRSVAN
KBA05_KRSZUL
KBA05_KW1
KBA05_KW2
KBA05_KW3
KBA05_MAXAH
KBA05_MAXBJ
KBA05_MAXHERST
KBA05_MAXSEG
KBA05_MAXVORB
KBA05_MOD1
KBA05_MOD2
KBA05_MOD3
KBA05_MOD4
KBA05_MOD8
KBA05_MODTEMP
KBA05_MOTOR
KBA05_MOTRAD
KBA05_SEG1
KBA05_SEG10
KBA05_SEG2
KBA05_SEG3
KBA05_SEG4
KBA05_SEG5
KBA05_SEG6
KBA05_SEG7
KBA05_SEG8
KBA05_SEG9
KBA05_VORB0
KBA05_VORB1
KBA05_VORB2
KBA05_ZUL1
KBA05_ZUL2
KBA05_ZUL3
KBA05_ZUL4
KBA13_ALTERHALTER_30
KBA13_ALTERHALTER_45
KBA13_ALTERHALTER_60
KBA13_ALTERHALTER_61
KBA13_AUDI
KBA13_AUTOQUOTE
KBA13_BJ_1999
KBA13_BJ_2000
KBA13_BJ_2004
KBA13_BJ_2006
KBA13_BJ_2008
KBA13_BJ_2009
KBA13_BMW
KBA13_CCM_1000
KBA13_CCM_1200
KBA13_CCM_1400
KBA13_CCM_0_1400
KBA13_CCM_1500
KBA13_CCM_1400_2500
KBA13_CCM_1600
KBA13_CCM_1800
KBA13_CCM_2000
KBA13_CCM_2500
KBA13_CCM_2501
KBA13_CCM_3000
KBA13_CCM_3001
KBA13_FAB_ASIEN
KBA13_FAB_SONSTIGE
KBA13_FIAT
KBA13_FORD
KBA13_HALTER_20
KBA13_HALTER_25
KBA13_HALTER_30
KBA13_HALTER_35
KBA13_HALTER_40
KBA13_HALTER_45
KBA13_HALTER_50
KBA13_HALTER_55
KBA13_HALTER_60
KBA13_HALTER_65
KBA13_HALTER_66
KBA13_HERST_ASIEN
KBA13_HERST_AUDI_VW
KBA13_HERST_BMW_BENZ
KBA13_HERST_EUROPA
KBA13_HERST_FORD_OPEL
KBA13_HERST_SONST
KBA13_KMH_110
KBA13_KMH_140
KBA13_KMH_180
KBA13_KMH_0_140
KBA13_KMH_140_210
KBA13_KMH_211
KBA13_KMH_250
KBA13_KMH_251
KBA13_KRSAQUOT
KBA13_KRSHERST_AUDI_VW
KBA13_KRSHERST_BMW_BENZ
KBA13_KRSHERST_FORD_OPEL
KBA13_KRSSEG_KLEIN
KBA13_KRSSEG_OBER
KBA13_KRSSEG_VAN
KBA13_KRSZUL_NEU
KBA13_KW_30
KBA13_KW_40
KBA13_KW_50
KBA13_KW_60
KBA13_KW_0_60
KBA13_KW_70
KBA13_KW_61_120
KBA13_KW_80
KBA13_KW_90
KBA13_KW_110
KBA13_KW_120
KBA13_KW_121
KBA13_MAZDA
KBA13_MERCEDES
KBA13_MOTOR
KBA13_NISSAN
KBA13_OPEL
KBA13_PEUGEOT
KBA13_RENAULT
KBA13_SEG_GELAENDEWAGEN
KBA13_SEG_GROSSRAUMVANS
KBA13_SEG_KLEINST
KBA13_SEG_KLEINWAGEN
KBA13_SEG_KOMPAKTKLASSE
KBA13_SEG_MINIVANS
KBA13_SEG_MINIWAGEN
KBA13_SEG_MITTELKLASSE
KBA13_SEG_OBEREMITTELKLASSE
KBA13_SEG_OBERKLASSE
KBA13_SEG_SONSTIGE
KBA13_SEG_SPORTWAGEN
KBA13_SEG_UTILITIES
KBA13_SEG_VAN
KBA13_SEG_WOHNMOBILE
KBA13_SITZE_4
KBA13_SITZE_5
KBA13_SITZE_6
KBA13_TOYOTA
KBA13_VORB_0
KBA13_VORB_1
KBA13_VORB_1_2
KBA13_VORB_2
KBA13_VORB_3
KBA13_VW
KKK
NATIONALITAET_KZ
ORTSGR_KLS9
OST_WEST_KZ
PLZ8_ANTG1
PLZ8_ANTG2
PLZ8_ANTG3
PLZ8_ANTG4
PLZ8_GBZ
PLZ8_HHZ
PRAEGENDE_JUGENDJAHRE
REGIOTYP
RELAT_AB
RETOURTYP_BK_S
SEMIO_DOM
SEMIO_ERL
SEMIO_FAM
SEMIO_KAEM
SEMIO_KRIT
SEMIO_KULT
SEMIO_LUST
SEMIO_MAT
SEMIO_PFLICHT
SEMIO_RAT
SEMIO_REL
SEMIO_SOZ
SEMIO_TRADV
SEMIO_VERT
SHOPPER_TYP
SOHO_FLAG
TITEL_KZ
VERS_TYP
WOHNDAUER_2008
WOHNLAGE
WACHSTUMSGEBIET_NB
W_KEIT_KIND_HH
ZABEOTYP
In [0]:
# Saving files to drive for further use
#%cd /content/gdrive/My Drive/Bertelsmann Data Science Project
#azdias = azdias.to_csv('azdias_with_missing')
#customers = customers.to_csv('customers_with_missing_values')
#nans = nans.to_csv('nans')
In [0]:
azdias.head()
Out[0]:
AGER_TYP ALTER_HH ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL ANZ_PERSONEN ANZ_TITEL BALLRAUM CAMEO_DEU_2015 CAMEO_DEUG_2015 CJT_GESAMTTYP D19_BANKEN_ANZ_12 D19_BANKEN_ANZ_24 D19_BANKEN_DATUM D19_BANKEN_OFFLINE_DATUM D19_BANKEN_ONLINE_DATUM D19_BANKEN_ONLINE_QUOTE_12 D19_GESAMT_ANZ_12 D19_GESAMT_ANZ_24 D19_GESAMT_DATUM D19_GESAMT_OFFLINE_DATUM D19_GESAMT_ONLINE_DATUM D19_GESAMT_ONLINE_QUOTE_12 D19_KONSUMTYP D19_TELKO_ANZ_12 D19_TELKO_ANZ_24 D19_TELKO_DATUM D19_TELKO_OFFLINE_DATUM D19_TELKO_ONLINE_DATUM D19_VERSAND_ANZ_12 D19_VERSAND_ANZ_24 D19_VERSAND_DATUM D19_VERSAND_OFFLINE_DATUM D19_VERSAND_ONLINE_DATUM D19_VERSAND_ONLINE_QUOTE_12 D19_VERSI_ANZ_12 D19_VERSI_ANZ_24 EWDICHTE FINANZ_ANLEGER FINANZ_HAUSBAUER FINANZ_MINIMALIST ... MIN_GEBAEUDEJAHR MOBI_REGIO NATIONALITAET_KZ ONLINE_AFFINITAET ORTSGR_KLS9 OST_WEST_KZ PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_BAUMAX PLZ8_GBZ PLZ8_HHZ PRAEGENDE_JUGENDJAHRE REGIOTYP RELAT_AB RETOURTYP_BK_S SEMIO_DOM SEMIO_ERL SEMIO_FAM SEMIO_KAEM SEMIO_KRIT SEMIO_KULT SEMIO_LUST SEMIO_MAT SEMIO_PFLICHT SEMIO_RAT SEMIO_REL SEMIO_SOZ SEMIO_TRADV SEMIO_VERT SHOPPER_TYP TITEL_KZ VERS_TYP W_KEIT_KIND_HH WOHNDAUER_2008 WOHNLAGE ZABEOTYP ANREDE_KZ ALTERSKATEGORIE_GROB
0 NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 5.0 3.0 3.0 ... NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 5.0 6.0 3.0 6.0 6.0 7.0 3.0 5.0 5.0 5.0 4.0 7.0 2.0 3.0 1.0 NaN NaN NaN NaN NaN NaN 3.0 1.0 2.0
1 NaN NaN 11.0 0.0 2.0 0.0 6.0 8A 8.0 5.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 3.0 5.0 5.0 1.0 ... 1992.0 1.0 1.0 3.0 5.0 W 2.0 3.0 2.0 1.0 1.0 4.0 5.0 14.0 3.0 4.0 1.0 7.0 2.0 4.0 4.0 4.0 3.0 2.0 3.0 7.0 6.0 4.0 5.0 6.0 1.0 3.0 NaN 2.0 3.0 9.0 4.0 5.0 2.0 1.0
2 NaN 17.0 10.0 0.0 1.0 0.0 2.0 4C 4.0 3.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 9.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 4.0 2.0 5.0 1.0 ... 1992.0 3.0 1.0 2.0 5.0 W 3.0 3.0 1.0 0.0 1.0 4.0 4.0 15.0 2.0 2.0 3.0 7.0 6.0 1.0 7.0 7.0 3.0 4.0 3.0 3.0 4.0 3.0 4.0 3.0 4.0 2.0 NaN 1.0 3.0 9.0 2.0 5.0 2.0 3.0
3 2.0 13.0 1.0 0.0 0.0 0.0 4.0 2A 2.0 2.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 9.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 2.0 2.0 4.0 ... 1997.0 4.0 1.0 1.0 3.0 W 2.0 2.0 2.0 0.0 1.0 4.0 3.0 8.0 NaN 3.0 2.0 4.0 7.0 1.0 5.0 4.0 4.0 4.0 1.0 4.0 3.0 2.0 5.0 4.0 4.0 1.0 NaN 1.0 NaN 9.0 7.0 3.0 2.0 4.0
4 NaN 20.0 3.0 0.0 4.0 0.0 2.0 6B 6.0 5.0 3.0 5.0 5.0 NaN 5.0 10.0 6.0 6.0 1.0 6.0 1.0 10.0 1.0 NaN 1.0 6.0 8.0 NaN 6.0 6.0 1.0 9.0 1.0 10.0 1.0 3.0 5.0 1.0 2.0 4.0 ... 1992.0 3.0 1.0 5.0 6.0 W 2.0 4.0 2.0 1.0 2.0 3.0 3.0 8.0 5.0 5.0 5.0 2.0 4.0 4.0 2.0 3.0 6.0 4.0 2.0 4.0 2.0 4.0 6.0 2.0 7.0 2.0 NaN 2.0 2.0 9.0 3.0 4.0 1.0 3.0

5 rows × 272 columns

In [0]:
azdias.describe()
Out[0]:
AGER_TYP ALTER_HH ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL ANZ_PERSONEN ANZ_TITEL BALLRAUM CJT_GESAMTTYP D19_BANKEN_ANZ_12 D19_BANKEN_ANZ_24 D19_BANKEN_DATUM D19_BANKEN_OFFLINE_DATUM D19_BANKEN_ONLINE_DATUM D19_BANKEN_ONLINE_QUOTE_12 D19_GESAMT_ANZ_12 D19_GESAMT_ANZ_24 D19_GESAMT_DATUM D19_GESAMT_OFFLINE_DATUM D19_GESAMT_ONLINE_DATUM D19_GESAMT_ONLINE_QUOTE_12 D19_KONSUMTYP D19_TELKO_ANZ_12 D19_TELKO_ANZ_24 D19_TELKO_DATUM D19_TELKO_OFFLINE_DATUM D19_TELKO_ONLINE_DATUM D19_VERSAND_ANZ_12 D19_VERSAND_ANZ_24 D19_VERSAND_DATUM D19_VERSAND_OFFLINE_DATUM D19_VERSAND_ONLINE_DATUM D19_VERSAND_ONLINE_QUOTE_12 D19_VERSI_ANZ_12 D19_VERSI_ANZ_24 EWDICHTE FINANZ_ANLEGER FINANZ_HAUSBAUER FINANZ_MINIMALIST FINANZ_SPARER FINANZ_UNAUFFAELLIGER ... LP_STATUS_GROB MIN_GEBAEUDEJAHR MOBI_REGIO NATIONALITAET_KZ ONLINE_AFFINITAET ORTSGR_KLS9 PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_BAUMAX PLZ8_GBZ PLZ8_HHZ PRAEGENDE_JUGENDJAHRE REGIOTYP RELAT_AB RETOURTYP_BK_S SEMIO_DOM SEMIO_ERL SEMIO_FAM SEMIO_KAEM SEMIO_KRIT SEMIO_KULT SEMIO_LUST SEMIO_MAT SEMIO_PFLICHT SEMIO_RAT SEMIO_REL SEMIO_SOZ SEMIO_TRADV SEMIO_VERT SHOPPER_TYP TITEL_KZ VERS_TYP W_KEIT_KIND_HH WOHNDAUER_2008 WOHNLAGE ZABEOTYP ANREDE_KZ ALTERSKATEGORIE_GROB
count 205378.000000 580954.000000 798073.000000 794213.000000 817722.000000 817722.000000 797481.000000 886367.000000 59487.000000 97121.000000 212890.000000 19686.000000 164239.000000 45234.000000 306424.000000 385918.000000 537051.000000 332663.000000 440226.000000 241033.000000 634108.000000 33231.000000 65013.000000 225423.000000 72107.000000 8203.000000 253249.000000 327403.000000 453335.000000 256988.000000 396757.000000 216741.000000 69932.000000 114184.000000 797481.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 ... 886367.000000 798073.000000 757897.000000 782906.000000 886367.000000 794005.000000 774706.000000 774706.000000 774706.000000 774706.000000 774706.000000 774706.000000 774706.000000 783057.000000 733157.000000 793846.000000 886367.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 780025.000000 2160.000000 780025.000000 743233.000000 817722.000000 791123.000000 891221.000000 891221.000000 891221.000000
mean 1.743410 15.291805 8.287263 0.040647 1.727637 0.004162 4.153043 3.632838 1.832804 2.017957 6.933200 6.685817 6.956204 9.886059 2.320027 2.864013 5.140640 7.413848 5.304019 9.368128 5.424540 1.315639 1.354437 7.741446 7.874617 8.020480 2.127839 2.610126 5.512548 7.665440 5.378252 9.492067 1.447692 1.615647 3.939172 3.033328 3.075121 3.074528 2.821039 2.874167 ... 2.432575 1993.277011 2.963540 1.168889 2.698691 5.293002 2.253330 2.801858 1.595426 0.699166 1.943913 3.381087 3.612821 9.280709 4.472086 3.071033 3.419630 4.667550 4.481405 4.272729 4.445007 4.763223 4.025014 4.359086 4.001597 4.256076 3.910139 4.240609 3.945860 3.661784 4.023709 1.590134 1.318519 1.511166 4.147141 7.908791 4.088440 3.362438 1.522098 2.777398
std 0.674312 3.800536 15.628087 0.324028 1.155849 0.068855 2.183710 1.595021 1.080954 1.226588 2.335306 2.421747 2.332829 0.765394 1.272181 1.497983 2.782871 2.047270 2.747413 1.640414 3.234275 0.632176 0.650309 1.825844 1.650424 1.558834 1.190232 1.434433 2.770594 1.863684 2.756699 1.497877 0.691940 0.834033 1.718996 1.529603 1.353248 1.321055 1.464749 1.486731 ... 1.474315 3.332739 1.428882 0.475075 1.521524 2.303739 0.972008 0.920309 0.986736 0.727137 1.459654 1.111598 0.973967 4.032107 1.836357 1.360532 1.417741 1.795712 1.807552 1.915885 1.852412 1.830789 1.903816 2.022829 1.857540 1.770137 1.580306 2.007373 1.946564 1.707637 2.077746 1.027972 0.999504 0.499876 1.784211 1.923137 1.920553 1.352704 0.499512 1.068775
min 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 ... 1.000000 1985.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
25% 1.000000 13.000000 1.000000 0.000000 1.000000 0.000000 2.000000 2.000000 1.000000 1.000000 5.000000 5.000000 5.000000 10.000000 1.000000 2.000000 2.000000 6.000000 3.000000 10.000000 2.000000 1.000000 1.000000 7.000000 8.000000 8.000000 1.000000 1.000000 3.000000 7.000000 3.000000 10.000000 1.000000 1.000000 2.000000 2.000000 2.000000 2.000000 1.000000 2.000000 ... 1.000000 1992.000000 2.000000 1.000000 1.000000 4.000000 1.000000 2.000000 1.000000 0.000000 1.000000 3.000000 3.000000 6.000000 3.000000 2.000000 2.000000 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000 2.000000 2.000000 3.000000 3.000000 3.000000 2.000000 2.000000 2.000000 1.000000 1.000000 1.000000 3.000000 8.000000 3.000000 3.000000 1.000000 2.000000
50% 2.000000 16.000000 4.000000 0.000000 1.000000 0.000000 5.000000 4.000000 1.000000 2.000000 8.000000 8.000000 8.000000 10.000000 2.000000 3.000000 5.000000 8.000000 5.000000 10.000000 5.000000 1.000000 1.000000 9.000000 9.000000 9.000000 2.000000 2.000000 5.000000 8.000000 5.000000 10.000000 1.000000 1.000000 4.000000 3.000000 3.000000 3.000000 3.000000 3.000000 ... 2.000000 1992.000000 3.000000 1.000000 3.000000 5.000000 2.000000 3.000000 2.000000 1.000000 1.000000 3.000000 4.000000 9.000000 5.000000 3.000000 3.000000 5.000000 4.000000 4.000000 5.000000 5.000000 4.000000 5.000000 4.000000 4.000000 4.000000 4.000000 4.000000 3.000000 4.000000 2.000000 1.000000 2.000000 4.000000 9.000000 4.000000 3.000000 2.000000 3.000000
75% 2.000000 18.000000 9.000000 0.000000 2.000000 0.000000 6.000000 5.000000 2.000000 3.000000 9.000000 9.000000 9.000000 10.000000 3.000000 4.000000 8.000000 9.000000 8.000000 10.000000 9.000000 2.000000 2.000000 9.000000 9.000000 9.000000 3.000000 4.000000 8.000000 9.000000 8.000000 10.000000 2.000000 2.000000 6.000000 5.000000 4.000000 4.000000 4.000000 4.000000 ... 4.000000 1993.000000 4.000000 1.000000 4.000000 7.000000 3.000000 3.000000 2.000000 1.000000 3.000000 4.000000 4.000000 14.000000 6.000000 4.000000 5.000000 6.000000 6.000000 6.000000 6.000000 6.000000 5.000000 6.000000 5.000000 6.000000 5.000000 6.000000 6.000000 5.000000 6.000000 2.000000 1.000000 2.000000 6.000000 9.000000 5.000000 4.000000 2.000000 4.000000
max 3.000000 21.000000 595.000000 23.000000 45.000000 6.000000 7.000000 6.000000 6.000000 6.000000 9.000000 9.000000 9.000000 10.000000 6.000000 6.000000 9.000000 9.000000 9.000000 10.000000 9.000000 6.000000 6.000000 9.000000 9.000000 9.000000 6.000000 6.000000 9.000000 9.000000 9.000000 10.000000 6.000000 6.000000 6.000000 5.000000 5.000000 5.000000 5.000000 5.000000 ... 5.000000 2016.000000 6.000000 3.000000 5.000000 9.000000 4.000000 4.000000 3.000000 2.000000 5.000000 5.000000 5.000000 15.000000 7.000000 5.000000 5.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 3.000000 5.000000 2.000000 6.000000 9.000000 8.000000 6.000000 2.000000 9.000000

8 rows × 269 columns

In [0]:
customers.head()
Out[0]:
AGER_TYP ALTER_HH ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL ANZ_PERSONEN ANZ_TITEL BALLRAUM CAMEO_DEU_2015 CAMEO_DEUG_2015 CJT_GESAMTTYP D19_BANKEN_ANZ_12 D19_BANKEN_ANZ_24 D19_BANKEN_DATUM D19_BANKEN_OFFLINE_DATUM D19_BANKEN_ONLINE_DATUM D19_BANKEN_ONLINE_QUOTE_12 D19_GESAMT_ANZ_12 D19_GESAMT_ANZ_24 D19_GESAMT_DATUM D19_GESAMT_OFFLINE_DATUM D19_GESAMT_ONLINE_DATUM D19_GESAMT_ONLINE_QUOTE_12 D19_KONSUMTYP D19_TELKO_ANZ_12 D19_TELKO_ANZ_24 D19_TELKO_DATUM D19_TELKO_OFFLINE_DATUM D19_TELKO_ONLINE_DATUM D19_VERSAND_ANZ_12 D19_VERSAND_ANZ_24 D19_VERSAND_DATUM D19_VERSAND_OFFLINE_DATUM D19_VERSAND_ONLINE_DATUM D19_VERSAND_ONLINE_QUOTE_12 D19_VERSI_ANZ_12 D19_VERSI_ANZ_24 EWDICHTE FINANZ_ANLEGER FINANZ_HAUSBAUER FINANZ_MINIMALIST ... ONLINE_AFFINITAET ORTSGR_KLS9 OST_WEST_KZ PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_BAUMAX PLZ8_GBZ PLZ8_HHZ PRAEGENDE_JUGENDJAHRE REGIOTYP RELAT_AB RETOURTYP_BK_S SEMIO_DOM SEMIO_ERL SEMIO_FAM SEMIO_KAEM SEMIO_KRIT SEMIO_KULT SEMIO_LUST SEMIO_MAT SEMIO_PFLICHT SEMIO_RAT SEMIO_REL SEMIO_SOZ SEMIO_TRADV SEMIO_VERT SHOPPER_TYP TITEL_KZ VERS_TYP W_KEIT_KIND_HH WOHNDAUER_2008 WOHNLAGE ZABEOTYP PRODUCT_GROUP CUSTOMER_GROUP ONLINE_PURCHASE ANREDE_KZ ALTERSKATEGORIE_GROB
0 2.0 10.0 1.0 0.0 2.0 0.0 3.0 1A 1.0 5.0 NaN NaN NaN NaN NaN NaN NaN NaN 9.0 9.0 NaN NaN 3.0 NaN NaN NaN NaN NaN NaN NaN 9.0 9.0 NaN NaN NaN NaN 2.0 1.0 2.0 5.0 ... 3.0 2.0 W 3.0 3.0 1.0 0.0 1.0 5.0 5.0 4.0 1.0 1.0 5.0 1.0 3.0 5.0 1.0 3.0 4.0 7.0 6.0 2.0 1.0 2.0 6.0 1.0 6.0 3.0 NaN 1.0 6.0 9.0 7.0 3.0 COSMETIC_AND_FOOD MULTI_BUYER 0 1.0 4.0
1 NaN 11.0 NaN NaN 3.0 0.0 NaN NaN NaN NaN NaN 1.0 6.0 NaN NaN NaN NaN 1.0 6.0 NaN 9.0 NaN 5.0 NaN NaN NaN NaN NaN NaN NaN 9.0 NaN 9.0 NaN NaN NaN NaN 1.0 2.0 5.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 3.0 3.0 6.0 2.0 3.0 4.0 5.0 6.0 4.0 1.0 2.0 3.0 1.0 7.0 3.0 NaN 1.0 NaN 9.0 NaN 3.0 FOOD SINGLE_BUYER 0 1.0 4.0
2 NaN 6.0 1.0 0.0 1.0 0.0 7.0 5D 5.0 2.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 3.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 4.0 1.0 4.0 5.0 ... 1.0 5.0 W 2.0 3.0 3.0 1.0 3.0 2.0 3.0 4.0 7.0 3.0 5.0 5.0 7.0 2.0 6.0 7.0 1.0 7.0 3.0 4.0 2.0 1.0 2.0 1.0 3.0 1.0 NaN 2.0 6.0 9.0 2.0 3.0 COSMETIC_AND_FOOD MULTI_BUYER 0 2.0 4.0
3 1.0 8.0 0.0 NaN 0.0 0.0 7.0 4C 4.0 2.0 NaN NaN NaN NaN NaN NaN NaN 1.0 6.0 6.0 NaN NaN 3.0 NaN NaN NaN NaN NaN NaN 1.0 6.0 6.0 NaN NaN NaN NaN 1.0 2.0 2.0 5.0 ... 2.0 3.0 W 3.0 2.0 1.0 0.0 1.0 4.0 3.0 1.0 6.0 1.0 3.0 3.0 3.0 5.0 3.0 3.0 4.0 5.0 4.0 3.0 3.0 3.0 6.0 4.0 7.0 0.0 NaN 1.0 NaN 9.0 7.0 1.0 COSMETIC MULTI_BUYER 0 1.0 4.0
4 NaN 20.0 7.0 0.0 4.0 0.0 3.0 7B 7.0 6.0 1.0 2.0 3.0 NaN 7.0 NaN 3.0 5.0 1.0 8.0 1.0 10.0 1.0 NaN 1.0 7.0 NaN 9.0 3.0 5.0 1.0 8.0 1.0 10.0 NaN NaN 4.0 4.0 2.0 3.0 ... 5.0 5.0 W 2.0 4.0 2.0 1.0 2.0 3.0 3.0 8.0 7.0 1.0 5.0 5.0 4.0 5.0 2.0 3.0 5.0 6.0 6.0 5.0 5.0 4.0 4.0 4.0 5.0 1.0 NaN 2.0 2.0 9.0 3.0 1.0 FOOD MULTI_BUYER 0 1.0 3.0

5 rows × 275 columns

In [0]:
azdias.shape,customers.shape
Out[0]:
((891221, 272), (191652, 275))
In [0]:
# dropping the 3 extra columns in customers
coll = [col for col in customers.columns if col not in azdias.columns]
customers.drop(columns=coll,inplace=True)
In [0]:
azdias.shape,customers.shape
Out[0]:
((891221, 272), (191652, 272))
In [0]:
# converting all integer values to float values for consistency.
def fltconv(df):
  for col in df.columns:
      if df[col].dtype == np.int64:
          df[col] = df[col].astype(np.float64)
In [0]:
fltconv(azdias)
fltconv(customers)
In [0]:
nan_in_cols = azdias.shape[0] - azdias.count()
In [0]:
plt.title('Distribution of missing values across population columns',fontdict = {'fontsize' : 20})
nan_in_cols.hist(bins=50);
In [0]:
# dropping columns with more than 200000 missing values in azdias.
drop_cols = azdias.columns[nan_in_cols > 200000]
print(drop_cols)
azdias.drop(drop_cols, axis=1, inplace = True)
azdias.shape
Index(['AGER_TYP', 'ALTER_HH', 'D19_BANKEN_ANZ_12', 'D19_BANKEN_ANZ_24',
       'D19_BANKEN_DATUM', 'D19_BANKEN_OFFLINE_DATUM',
       'D19_BANKEN_ONLINE_DATUM', 'D19_BANKEN_ONLINE_QUOTE_12',
       'D19_GESAMT_ANZ_12', 'D19_GESAMT_ANZ_24', 'D19_GESAMT_DATUM',
       'D19_GESAMT_OFFLINE_DATUM', 'D19_GESAMT_ONLINE_DATUM',
       'D19_GESAMT_ONLINE_QUOTE_12', 'D19_KONSUMTYP', 'D19_TELKO_ANZ_12',
       'D19_TELKO_ANZ_24', 'D19_TELKO_DATUM', 'D19_TELKO_OFFLINE_DATUM',
       'D19_TELKO_ONLINE_DATUM', 'D19_VERSAND_ANZ_12', 'D19_VERSAND_ANZ_24',
       'D19_VERSAND_DATUM', 'D19_VERSAND_OFFLINE_DATUM',
       'D19_VERSAND_ONLINE_DATUM', 'D19_VERSAND_ONLINE_QUOTE_12',
       'D19_VERSI_ANZ_12', 'D19_VERSI_ANZ_24', 'KBA05_ANTG1', 'KBA05_ANTG2',
       'KBA05_ANTG3', 'KBA05_ANTG4', 'KBA05_BAUMAX', 'KBA05_MAXVORB',
       'TITEL_KZ'],
      dtype='object')
Out[0]:
(891221, 237)
In [0]:
nan_in_cols2 = customers.shape[0] - customers.count()
In [0]:
plt.title('Distribution of missing values across customers columns',fontdict = {'fontsize' : 20})
nan_in_cols2.hist(bins=50);
In [0]:
# dropping columns with more than 65000 missing values in customers.
drop_cols2 = customers.columns[nan_in_cols2 > 65000]
print(drop_cols2)
customers.drop(drop_cols, axis=1, inplace = True)
customers.shape
Index(['AGER_TYP', 'ALTER_HH', 'D19_BANKEN_ANZ_12', 'D19_BANKEN_ANZ_24',
       'D19_BANKEN_DATUM', 'D19_BANKEN_OFFLINE_DATUM',
       'D19_BANKEN_ONLINE_DATUM', 'D19_BANKEN_ONLINE_QUOTE_12',
       'D19_GESAMT_ANZ_12', 'D19_GESAMT_ANZ_24', 'D19_GESAMT_OFFLINE_DATUM',
       'D19_GESAMT_ONLINE_DATUM', 'D19_GESAMT_ONLINE_QUOTE_12',
       'D19_TELKO_ANZ_12', 'D19_TELKO_ANZ_24', 'D19_TELKO_DATUM',
       'D19_TELKO_OFFLINE_DATUM', 'D19_TELKO_ONLINE_DATUM',
       'D19_VERSAND_ANZ_12', 'D19_VERSAND_ANZ_24', 'D19_VERSAND_DATUM',
       'D19_VERSAND_OFFLINE_DATUM', 'D19_VERSAND_ONLINE_DATUM',
       'D19_VERSAND_ONLINE_QUOTE_12', 'D19_VERSI_ANZ_12', 'D19_VERSI_ANZ_24',
       'KBA05_ANTG1', 'KBA05_ANTG2', 'KBA05_ANTG3', 'KBA05_ANTG4',
       'KBA05_BAUMAX', 'KBA05_MAXVORB', 'TITEL_KZ'],
      dtype='object')
Out[0]:
(191652, 237)
In [0]:
nan_in_rows = azdias.shape[1] - azdias.count(axis = 1)
In [0]:
plt.title('Distribution of missing values across population rows',fontdict = {'fontsize' : 20})
nan_in_rows.hist(bins=20);
In [0]:
# dropping rows with more than 50 missing values in azdias.
drop_rows = azdias.index[nan_in_rows > 50]
print(drop_rows)
azdias.drop(drop_rows, axis=0, inplace = True)
azdias.shape
Int64Index([     0,     11,     12,     13,     14,     15,     17,     20,
                23,     24,
            ...
            891164, 891169, 891170, 891171, 891172, 891173, 891175, 891185,
            891187, 891203],
           dtype='int64', length=153933)
Out[0]:
(737288, 237)
In [0]:
nan_in_rows2 = customers.shape[1] - customers.count(axis = 1)
In [0]:
plt.title('Distribution of missing values across customer rows',fontdict = {'fontsize' : 20})
nan_in_rows2.hist(bins=20);
In [0]:
# dropping rows with more than 50 missing values in customers.
drop_rows2 = customers.index[nan_in_rows2 > 50]
print(drop_rows2)
customers.drop(drop_rows2, axis=0, inplace = True)
customers.shape
Int64Index([     1,     10,     13,     20,     36,     37,     38,     39,
                40,     43,
            ...
            191494, 191527, 191541, 191553, 191565, 191570, 191575, 191597,
            191605, 191639],
           dtype='int64', length=57406)
Out[0]:
(134246, 237)
In [0]:
# Re-encoding the only binary variable found.
azdias['OST_WEST_KZ'].replace(['W', 'O'], [1, 0], inplace=True)
customers['OST_WEST_KZ'].replace(['W', 'O'], [1, 0], inplace=True)
In [0]:
#dropping columns with greater than 10 different values for simplicity.
cat_cols_to_drop = ['CAMEO_DEU_2015', 'GFK_URLAUBERTYP', 'LP_FAMILIE_FEIN', 'LP_LEBENSPHASE_FEIN', 
                   'LP_LEBENSPHASE_GROB', 'LP_STATUS_FEIN', 'PRAEGENDE_JUGENDJAHRE','CAMEO_DEUG_2015']
# Doing one-hot encoding on columns with less than 10 different values.
dummy_cols = ['CJT_GESAMTTYP', 'FINANZTYP', 'GEBAEUDETYP', 'GEBAEUDETYP_RASTER', 'HEALTH_TYP', 
                    'KBA05_HERSTTEMP', 'KBA05_MAXHERST', 'KBA05_MODTEMP', 'LP_FAMILIE_GROB', 'LP_STATUS_GROB',
                    'NATIONALITAET_KZ', 'SHOPPER_TYP', 'VERS_TYP']
In [0]:
azdias.drop(columns=cat_cols_to_drop,inplace=True,axis=1)
customers.drop(columns=cat_cols_to_drop,inplace=True,axis=1)
In [0]:
# Create dummy variables for columns with less than 10 
# unique values then drop the original columns
def dummies(df):
  for col in dummy_cols:
      dummy = pd.get_dummies(df[col], prefix = col)
      df = pd.concat([df, dummy], axis = 1)
  print(df.shape)
  df.drop(dummy_cols, axis=1, inplace = True)
  print(df.shape)
In [0]:
dummies(azdias)
dummies(customers)
(737288, 292)
(737288, 279)
(134246, 291)
(134246, 278)
In [0]:
# Test to see if columns are alike
for cols in azdias.columns:
  if cols not in customers.columns:
    print(cols)
In [0]:
# Mean is selected for imputation as after reiterations, it was found to be more useful than median or most frequent.
def impute(df):
  imputer = SimpleImputer(missing_values=np.nan,strategy='mean')
  return pd.DataFrame(imputer.fit_transform(df), columns = df.columns)
In [0]:
azdias = impute(azdias)
customers = impute(customers)
In [0]:
azdias.shape, customers.shape
Out[0]:
((737288, 229), (134246, 229))
In [0]:
# Test to see if there are still any missing values in azdias
(azdias.shape[0] - azdias.count()).sort_values()
Out[0]:
ANZ_HAUSHALTE_AKTIV            0
KBA13_KW_120                   0
KBA13_KW_121                   0
KBA13_KW_30                    0
KBA13_KW_40                    0
KBA13_KW_50                    0
KBA13_KW_60                    0
KBA13_KW_61_120                0
KBA13_KW_70                    0
KBA13_KW_80                    0
KBA13_KW_90                    0
KBA13_MAZDA                    0
KBA13_KW_110                   0
KBA13_MERCEDES                 0
KBA13_NISSAN                   0
KBA13_OPEL                     0
KBA13_PEUGEOT                  0
KBA13_RENAULT                  0
KBA13_SEG_GELAENDEWAGEN        0
KBA13_SEG_GROSSRAUMVANS        0
KBA13_SEG_KLEINST              0
KBA13_SEG_KLEINWAGEN           0
KBA13_SEG_KOMPAKTKLASSE        0
KBA13_SEG_MINIVANS             0
KBA13_SEG_MINIWAGEN            0
KBA13_MOTOR                    0
KBA13_KW_0_60                  0
KBA13_KRSZUL_NEU               0
KBA13_KRSSEG_VAN               0
KBA13_HALTER_50                0
KBA13_HALTER_55                0
KBA13_HALTER_60                0
KBA13_HALTER_65                0
KBA13_HALTER_66                0
KBA13_HERST_ASIEN              0
KBA13_HERST_AUDI_VW            0
KBA13_HERST_BMW_BENZ           0
KBA13_HERST_EUROPA             0
KBA13_HERST_FORD_OPEL          0
KBA13_HERST_SONST              0
KBA13_KMH_0_140                0
KBA13_KMH_110                  0
KBA13_KMH_140                  0
KBA13_KMH_140_210              0
KBA13_KMH_180                  0
KBA13_KMH_211                  0
KBA13_KMH_250                  0
KBA13_KMH_251                  0
KBA13_KRSAQUOT                 0
KBA13_KRSHERST_AUDI_VW         0
KBA13_KRSHERST_BMW_BENZ        0
KBA13_KRSHERST_FORD_OPEL       0
KBA13_KRSSEG_KLEIN             0
KBA13_KRSSEG_OBER              0
KBA13_SEG_MITTELKLASSE         0
KBA13_SEG_OBEREMITTELKLASSE    0
KBA13_SEG_OBERKLASSE           0
KBA13_SEG_SONSTIGE             0
PLZ8_GBZ                       0
PLZ8_HHZ                       0
REGIOTYP                       0
RELAT_AB                       0
RETOURTYP_BK_S                 0
SEMIO_DOM                      0
SEMIO_ERL                      0
SEMIO_FAM                      0
SEMIO_KAEM                     0
SEMIO_KRIT                     0
SEMIO_KULT                     0
SEMIO_LUST                     0
SEMIO_MAT                      0
SEMIO_PFLICHT                  0
SEMIO_RAT                      0
SEMIO_REL                      0
SEMIO_SOZ                      0
SEMIO_TRADV                    0
SEMIO_VERT                     0
SHOPPER_TYP                    0
VERS_TYP                       0
W_KEIT_KIND_HH                 0
WOHNDAUER_2008                 0
WOHNLAGE                       0
ZABEOTYP                       0
PLZ8_BAUMAX                    0
KBA13_HALTER_45                0
PLZ8_ANTG4                     0
PLZ8_ANTG2                     0
KBA13_SEG_SPORTWAGEN           0
KBA13_SEG_UTILITIES            0
KBA13_SEG_VAN                  0
KBA13_SEG_WOHNMOBILE           0
KBA13_SITZE_4                  0
KBA13_SITZE_5                  0
KBA13_SITZE_6                  0
KBA13_TOYOTA                   0
KBA13_VORB_0                   0
KBA13_VORB_1                   0
KBA13_VORB_1_2                 0
KBA13_VORB_2                   0
KBA13_VORB_3                   0
KBA13_VW                       0
KKK                            0
KONSUMNAEHE                    0
LP_FAMILIE_GROB                0
LP_STATUS_GROB                 0
MIN_GEBAEUDEJAHR               0
MOBI_REGIO                     0
NATIONALITAET_KZ               0
ONLINE_AFFINITAET              0
ORTSGR_KLS9                    0
OST_WEST_KZ                    0
PLZ8_ANTG1                     0
PLZ8_ANTG3                     0
ANREDE_KZ                      0
KBA13_HALTER_40                0
KBA13_HALTER_30                0
KBA05_CCM4                     0
KBA05_DIESEL                   0
KBA05_FRAU                     0
KBA05_GBZ                      0
KBA05_HERST1                   0
KBA05_HERST2                   0
KBA05_HERST3                   0
KBA05_HERST4                   0
KBA05_HERST5                   0
KBA05_HERSTTEMP                0
KBA05_KRSAQUOT                 0
KBA05_CCM3                     0
KBA05_KRSHERST1                0
KBA05_KRSHERST3                0
KBA05_KRSKLEIN                 0
KBA05_KRSOBER                  0
KBA05_KRSVAN                   0
KBA05_KRSZUL                   0
KBA05_KW1                      0
KBA05_KW2                      0
KBA05_KW3                      0
KBA05_MAXAH                    0
KBA05_MAXBJ                    0
KBA05_MAXHERST                 0
KBA05_KRSHERST2                0
KBA05_CCM2                     0
KBA05_CCM1                     0
KBA05_AUTOQUOT                 0
ANZ_HH_TITEL                   0
ANZ_PERSONEN                   0
ANZ_TITEL                      0
BALLRAUM                       0
CJT_GESAMTTYP                  0
EWDICHTE                       0
FINANZ_ANLEGER                 0
FINANZ_HAUSBAUER               0
FINANZ_MINIMALIST              0
FINANZ_SPARER                  0
FINANZ_UNAUFFAELLIGER          0
FINANZ_VORSORGER               0
FINANZTYP                      0
GEBAEUDETYP                    0
GEBAEUDETYP_RASTER             0
GEBURTSJAHR                    0
GREEN_AVANTGARDE               0
HEALTH_TYP                     0
HH_EINKOMMEN_SCORE             0
INNENSTADT                     0
KBA05_ALTER1                   0
KBA05_ALTER2                   0
KBA05_ALTER3                   0
KBA05_ALTER4                   0
KBA05_ANHANG                   0
KBA05_MAXSEG                   0
KBA05_MOD1                     0
KBA05_MOD2                     0
KBA05_MOD3                     0
KBA13_BJ_1999                  0
KBA13_BJ_2000                  0
KBA13_BJ_2004                  0
KBA13_BJ_2006                  0
KBA13_BJ_2008                  0
KBA13_BJ_2009                  0
KBA13_BMW                      0
KBA13_CCM_0_1400               0
KBA13_CCM_1000                 0
KBA13_CCM_1200                 0
KBA13_CCM_1400                 0
KBA13_CCM_1500                 0
KBA13_CCM_1600                 0
KBA13_CCM_1800                 0
KBA13_CCM_2000                 0
KBA13_CCM_2500                 0
KBA13_CCM_2501                 0
KBA13_CCM_3000                 0
KBA13_CCM_3001                 0
KBA13_FAB_ASIEN                0
KBA13_FAB_SONSTIGE             0
KBA13_FIAT                     0
KBA13_FORD                     0
KBA13_HALTER_20                0
KBA13_HALTER_25                0
KBA13_AUTOQUOTE                0
KBA13_HALTER_35                0
KBA13_AUDI                     0
KBA13_ALTERHALTER_61           0
KBA05_MOD4                     0
KBA05_MOD8                     0
KBA05_MODTEMP                  0
KBA05_MOTOR                    0
KBA05_MOTRAD                   0
KBA05_SEG1                     0
KBA05_SEG10                    0
KBA05_SEG2                     0
KBA05_SEG3                     0
KBA05_SEG4                     0
KBA05_SEG5                     0
KBA05_SEG6                     0
KBA05_SEG7                     0
KBA05_SEG8                     0
KBA05_SEG9                     0
KBA05_VORB0                    0
KBA05_VORB1                    0
KBA05_VORB2                    0
KBA05_ZUL1                     0
KBA05_ZUL2                     0
KBA05_ZUL3                     0
KBA05_ZUL4                     0
KBA13_ALTERHALTER_30           0
KBA13_ALTERHALTER_45           0
KBA13_ALTERHALTER_60           0
KBA13_ANZAHL_PKW               0
ALTERSKATEGORIE_GROB           0
dtype: int64
In [0]:
# Test to see if there are still any missing values in customers
(customers.shape[0] - customers.count()).sort_values()
Out[0]:
ANZ_HAUSHALTE_AKTIV            0
KBA13_KW_120                   0
KBA13_KW_121                   0
KBA13_KW_30                    0
KBA13_KW_40                    0
KBA13_KW_50                    0
KBA13_KW_60                    0
KBA13_KW_61_120                0
KBA13_KW_70                    0
KBA13_KW_80                    0
KBA13_KW_90                    0
KBA13_MAZDA                    0
KBA13_KW_110                   0
KBA13_MERCEDES                 0
KBA13_NISSAN                   0
KBA13_OPEL                     0
KBA13_PEUGEOT                  0
KBA13_RENAULT                  0
KBA13_SEG_GELAENDEWAGEN        0
KBA13_SEG_GROSSRAUMVANS        0
KBA13_SEG_KLEINST              0
KBA13_SEG_KLEINWAGEN           0
KBA13_SEG_KOMPAKTKLASSE        0
KBA13_SEG_MINIVANS             0
KBA13_SEG_MINIWAGEN            0
KBA13_MOTOR                    0
KBA13_KW_0_60                  0
KBA13_KRSZUL_NEU               0
KBA13_KRSSEG_VAN               0
KBA13_HALTER_50                0
KBA13_HALTER_55                0
KBA13_HALTER_60                0
KBA13_HALTER_65                0
KBA13_HALTER_66                0
KBA13_HERST_ASIEN              0
KBA13_HERST_AUDI_VW            0
KBA13_HERST_BMW_BENZ           0
KBA13_HERST_EUROPA             0
KBA13_HERST_FORD_OPEL          0
KBA13_HERST_SONST              0
KBA13_KMH_0_140                0
KBA13_KMH_110                  0
KBA13_KMH_140                  0
KBA13_KMH_140_210              0
KBA13_KMH_180                  0
KBA13_KMH_211                  0
KBA13_KMH_250                  0
KBA13_KMH_251                  0
KBA13_KRSAQUOT                 0
KBA13_KRSHERST_AUDI_VW         0
KBA13_KRSHERST_BMW_BENZ        0
KBA13_KRSHERST_FORD_OPEL       0
KBA13_KRSSEG_KLEIN             0
KBA13_KRSSEG_OBER              0
KBA13_SEG_MITTELKLASSE         0
KBA13_SEG_OBEREMITTELKLASSE    0
KBA13_SEG_OBERKLASSE           0
KBA13_SEG_SONSTIGE             0
PLZ8_GBZ                       0
PLZ8_HHZ                       0
REGIOTYP                       0
RELAT_AB                       0
RETOURTYP_BK_S                 0
SEMIO_DOM                      0
SEMIO_ERL                      0
SEMIO_FAM                      0
SEMIO_KAEM                     0
SEMIO_KRIT                     0
SEMIO_KULT                     0
SEMIO_LUST                     0
SEMIO_MAT                      0
SEMIO_PFLICHT                  0
SEMIO_RAT                      0
SEMIO_REL                      0
SEMIO_SOZ                      0
SEMIO_TRADV                    0
SEMIO_VERT                     0
SHOPPER_TYP                    0
VERS_TYP                       0
W_KEIT_KIND_HH                 0
WOHNDAUER_2008                 0
WOHNLAGE                       0
ZABEOTYP                       0
PLZ8_BAUMAX                    0
KBA13_HALTER_45                0
PLZ8_ANTG4                     0
PLZ8_ANTG2                     0
KBA13_SEG_SPORTWAGEN           0
KBA13_SEG_UTILITIES            0
KBA13_SEG_VAN                  0
KBA13_SEG_WOHNMOBILE           0
KBA13_SITZE_4                  0
KBA13_SITZE_5                  0
KBA13_SITZE_6                  0
KBA13_TOYOTA                   0
KBA13_VORB_0                   0
KBA13_VORB_1                   0
KBA13_VORB_1_2                 0
KBA13_VORB_2                   0
KBA13_VORB_3                   0
KBA13_VW                       0
KKK                            0
KONSUMNAEHE                    0
LP_FAMILIE_GROB                0
LP_STATUS_GROB                 0
MIN_GEBAEUDEJAHR               0
MOBI_REGIO                     0
NATIONALITAET_KZ               0
ONLINE_AFFINITAET              0
ORTSGR_KLS9                    0
OST_WEST_KZ                    0
PLZ8_ANTG1                     0
PLZ8_ANTG3                     0
ANREDE_KZ                      0
KBA13_HALTER_40                0
KBA13_HALTER_30                0
KBA05_CCM4                     0
KBA05_DIESEL                   0
KBA05_FRAU                     0
KBA05_GBZ                      0
KBA05_HERST1                   0
KBA05_HERST2                   0
KBA05_HERST3                   0
KBA05_HERST4                   0
KBA05_HERST5                   0
KBA05_HERSTTEMP                0
KBA05_KRSAQUOT                 0
KBA05_CCM3                     0
KBA05_KRSHERST1                0
KBA05_KRSHERST3                0
KBA05_KRSKLEIN                 0
KBA05_KRSOBER                  0
KBA05_KRSVAN                   0
KBA05_KRSZUL                   0
KBA05_KW1                      0
KBA05_KW2                      0
KBA05_KW3                      0
KBA05_MAXAH                    0
KBA05_MAXBJ                    0
KBA05_MAXHERST                 0
KBA05_KRSHERST2                0
KBA05_CCM2                     0
KBA05_CCM1                     0
KBA05_AUTOQUOT                 0
ANZ_HH_TITEL                   0
ANZ_PERSONEN                   0
ANZ_TITEL                      0
BALLRAUM                       0
CJT_GESAMTTYP                  0
EWDICHTE                       0
FINANZ_ANLEGER                 0
FINANZ_HAUSBAUER               0
FINANZ_MINIMALIST              0
FINANZ_SPARER                  0
FINANZ_UNAUFFAELLIGER          0
FINANZ_VORSORGER               0
FINANZTYP                      0
GEBAEUDETYP                    0
GEBAEUDETYP_RASTER             0
GEBURTSJAHR                    0
GREEN_AVANTGARDE               0
HEALTH_TYP                     0
HH_EINKOMMEN_SCORE             0
INNENSTADT                     0
KBA05_ALTER1                   0
KBA05_ALTER2                   0
KBA05_ALTER3                   0
KBA05_ALTER4                   0
KBA05_ANHANG                   0
KBA05_MAXSEG                   0
KBA05_MOD1                     0
KBA05_MOD2                     0
KBA05_MOD3                     0
KBA13_BJ_1999                  0
KBA13_BJ_2000                  0
KBA13_BJ_2004                  0
KBA13_BJ_2006                  0
KBA13_BJ_2008                  0
KBA13_BJ_2009                  0
KBA13_BMW                      0
KBA13_CCM_0_1400               0
KBA13_CCM_1000                 0
KBA13_CCM_1200                 0
KBA13_CCM_1400                 0
KBA13_CCM_1500                 0
KBA13_CCM_1600                 0
KBA13_CCM_1800                 0
KBA13_CCM_2000                 0
KBA13_CCM_2500                 0
KBA13_CCM_2501                 0
KBA13_CCM_3000                 0
KBA13_CCM_3001                 0
KBA13_FAB_ASIEN                0
KBA13_FAB_SONSTIGE             0
KBA13_FIAT                     0
KBA13_FORD                     0
KBA13_HALTER_20                0
KBA13_HALTER_25                0
KBA13_AUTOQUOTE                0
KBA13_HALTER_35                0
KBA13_AUDI                     0
KBA13_ALTERHALTER_61           0
KBA05_MOD4                     0
KBA05_MOD8                     0
KBA05_MODTEMP                  0
KBA05_MOTOR                    0
KBA05_MOTRAD                   0
KBA05_SEG1                     0
KBA05_SEG10                    0
KBA05_SEG2                     0
KBA05_SEG3                     0
KBA05_SEG4                     0
KBA05_SEG5                     0
KBA05_SEG6                     0
KBA05_SEG7                     0
KBA05_SEG8                     0
KBA05_SEG9                     0
KBA05_VORB0                    0
KBA05_VORB1                    0
KBA05_VORB2                    0
KBA05_ZUL1                     0
KBA05_ZUL2                     0
KBA05_ZUL3                     0
KBA05_ZUL4                     0
KBA13_ALTERHALTER_30           0
KBA13_ALTERHALTER_45           0
KBA13_ALTERHALTER_60           0
KBA13_ANZAHL_PKW               0
ALTERSKATEGORIE_GROB           0
dtype: int64
In [0]:
# convert to int for next step
azdias = azdias.astype(int)
customers = customers.astype(int)
In [0]:
# detect and exclude rows with outlier in dataframe using method mentioned in:
# https://stackoverflow.com/questions/23199796/detect-and-exclude-outliers-in-pandas-data-frame
azdias = azdias[(np.abs(stats.zscore(azdias)) < 6).all(axis=1)]
customers = customers[(np.abs(stats.zscore(customers)) < 6).all(axis=1)]
In [0]:
azdias.shape,customers.shape
Out[0]:
((726430, 229), (129213, 229))
In [0]:
# Scaling values for normalization 
scaler = StandardScaler()
azdias = pd.DataFrame(scaler.fit_transform(azdias), columns = azdias.columns)
customers = pd.DataFrame(scaler.transform(customers), columns = customers.columns)
In [0]:
customers.head()
Out[0]:
ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL ANZ_PERSONEN ANZ_TITEL BALLRAUM CJT_GESAMTTYP EWDICHTE FINANZ_ANLEGER FINANZ_HAUSBAUER FINANZ_MINIMALIST FINANZ_SPARER FINANZ_UNAUFFAELLIGER FINANZ_VORSORGER FINANZTYP GEBAEUDETYP GEBAEUDETYP_RASTER GEBURTSJAHR GREEN_AVANTGARDE HEALTH_TYP HH_EINKOMMEN_SCORE INNENSTADT KBA05_ALTER1 KBA05_ALTER2 KBA05_ALTER3 KBA05_ALTER4 KBA05_ANHANG KBA05_AUTOQUOT KBA05_CCM1 KBA05_CCM2 KBA05_CCM3 KBA05_CCM4 KBA05_DIESEL KBA05_FRAU KBA05_GBZ KBA05_HERST1 KBA05_HERST2 KBA05_HERST3 KBA05_HERST4 KBA05_HERST5 KBA05_HERSTTEMP ... LP_FAMILIE_GROB LP_STATUS_GROB MIN_GEBAEUDEJAHR MOBI_REGIO NATIONALITAET_KZ ONLINE_AFFINITAET ORTSGR_KLS9 OST_WEST_KZ PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_BAUMAX PLZ8_GBZ PLZ8_HHZ REGIOTYP RELAT_AB RETOURTYP_BK_S SEMIO_DOM SEMIO_ERL SEMIO_FAM SEMIO_KAEM SEMIO_KRIT SEMIO_KULT SEMIO_LUST SEMIO_MAT SEMIO_PFLICHT SEMIO_RAT SEMIO_REL SEMIO_SOZ SEMIO_TRADV SEMIO_VERT SHOPPER_TYP VERS_TYP W_KEIT_KIND_HH WOHNDAUER_2008 WOHNLAGE ZABEOTYP ANREDE_KZ ALTERSKATEGORIE_GROB
0 -0.632714 -0.1462 0.230530 0.0 -0.528379 0.984757 -1.142375 -1.257692 -0.849526 1.417078 -1.154003 -0.436024 1.131878 -0.878295 -0.681843 0.276358 -1.253842 1.868900 -1.630398 -2.249555 -0.270461 0.055295 -0.974471 0.933943 0.969339 0.039948 1.661027 0.030065 0.000033 -0.024370 -0.164741 -0.954019 0.938196 0.613630 1.217323 0.012961 0.069771 -0.672625 -0.621032 -0.518812 ... -0.176514 1.700583 -0.398185 0.703120 -0.346992 0.185551 -1.447435 0.514608 0.768333 0.207562 -0.612187 -0.958666 -0.644619 1.467320 1.439079 -1.941597 -1.538098 1.079663 -1.978441 -0.885984 0.469442 -1.762217 -0.869649 -0.087507 1.257354 1.116286 -1.164749 -1.743572 -1.049131 0.934012 -1.546271 0.910278 1.451368 -1.004050 1.076320 0.548094 1.553998 -0.279453 -1.044483 1.094113
1 -0.632714 -0.1462 -0.641977 0.0 1.305268 -0.979015 0.027728 -1.257692 0.600368 1.417078 -1.154003 1.004470 1.131878 -0.878295 1.912321 -0.828924 -1.253842 1.868900 -0.278796 -2.249555 -1.753049 0.055295 0.907991 -0.929467 0.162682 2.154164 0.785459 -0.904345 -0.961161 1.828213 0.655212 1.875183 0.938196 -0.149103 1.217323 0.982870 -1.712837 -0.672625 0.153358 -0.518812 ... -0.755648 1.700583 -0.398185 -0.003697 -0.346992 -1.111776 -0.133193 0.514608 -0.262151 0.207562 1.423882 0.422115 0.736962 -1.250315 -0.630007 1.432840 -0.061982 1.079663 0.226941 1.298502 -1.113209 0.909469 1.432933 -1.632361 1.257354 -0.445853 -0.105717 -1.139579 -1.572575 -1.129545 -1.546271 -0.643369 -0.528158 0.995966 1.076320 0.548094 -1.083487 -0.279453 0.957411 1.094113
2 -0.731297 -0.1462 -1.514484 0.0 1.305268 -0.979015 -1.727427 -0.579581 -0.849526 1.417078 -1.154003 -1.156271 1.131878 1.025337 -0.311248 0.276358 -1.253842 -0.535074 -0.278796 -0.299208 1.212127 0.055295 -0.033240 0.002238 0.162682 2.154164 -0.090108 0.030065 0.961228 -0.950662 -0.984694 0.932116 -0.926813 0.613630 0.483671 -1.926858 0.069771 1.743100 0.153358 0.394470 ... -1.334781 1.038503 -0.398185 0.703120 -0.346992 -0.463113 -1.009355 0.514608 0.768333 -0.885882 -0.612187 -0.958666 -0.644619 0.561442 -0.630007 0.870434 -1.538098 -0.292306 -0.875750 -0.885984 0.469442 -0.693542 -0.869649 -0.087507 0.300324 0.074860 -0.635233 -0.535585 -0.525687 0.934012 0.156465 1.428161 -1.517921 -1.004050 -0.099781 0.548094 1.553998 -1.699860 -1.044483 1.094113
3 -0.041213 -0.1462 1.975544 0.0 -0.528379 1.639348 0.027728 0.776640 -0.849526 -0.030023 -1.154003 1.724717 0.405907 -0.878295 0.059347 -0.828924 0.790124 -0.535074 1.072807 1.001023 -0.270461 0.055295 0.907991 0.933943 -1.450632 -1.017159 -0.090108 -0.904345 1.922422 -0.950662 -0.984694 -0.010952 0.005692 -0.149103 -0.983632 0.982870 0.069771 -0.672625 0.153358 -1.432094 ... 1.560885 -0.947735 -0.398185 -0.003697 -0.346992 1.482878 -0.133193 0.514608 -0.262151 1.301006 0.405847 0.422115 0.046172 -0.344437 -0.630007 1.432840 -1.538098 1.079663 0.226941 -0.339862 0.469442 -1.227879 -0.869649 0.427444 0.778839 1.116286 0.423800 0.672401 -0.002242 -0.097766 0.156465 0.392396 -0.528158 0.995966 -1.275881 0.548094 -0.555990 -1.699860 -1.044483 0.169218
4 -0.632714 -0.1462 0.230530 0.0 1.305268 0.330166 0.612779 -1.257692 -0.124579 1.417078 -1.154003 -0.436024 1.131878 0.549429 -0.681843 0.276358 -1.253842 1.868900 1.072807 -2.249555 1.706323 -1.626044 -0.974471 0.002238 1.775995 -1.017159 -0.090108 0.030065 -0.961161 -0.024370 2.295118 -0.954019 1.870701 -0.149103 0.483671 0.982870 -1.712837 0.132617 0.153358 -0.518812 ... -0.176514 0.376424 -0.398185 -0.003697 -0.346992 0.185551 0.742969 0.514608 -0.262151 0.207562 0.405847 0.422115 -0.644619 1.467320 1.439079 -0.816785 1.414135 -0.292306 0.226941 -0.339862 -0.058108 -0.693542 0.281642 0.942396 0.778839 -1.487278 -0.635233 -1.139579 -0.002242 0.934012 0.156465 1.428161 -0.528158 0.995966 1.076320 0.548094 -1.610984 -0.989657 -1.044483 0.169218

5 rows × 229 columns

Unsupervised Learning Model

In [0]:
pca = PCA().fit(azdias)
plt.figure()
plt.plot(np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('No. of Components')
plt.ylabel('Variance Explained (%)')
plt.show()
In [0]:
def pca_transform(df,n):
    global pca
    pca = PCA(n_components=n)
    transformed_data = pd.DataFrame(pca.fit_transform(df))
    print(pca.explained_variance_ratio_.sum())
    return transformed_data
In [0]:
# selecting 120 n-components based on above chart as it retains more than 90% variance.
%%time
azdias_pca = pca_transform(azdias, n=120)
customers_pca = pca_transform(customers, n=120)
0.9060771214882293
0.9042891190068887
CPU times: user 2min 1s, sys: 26.7 s, total: 2min 28s
Wall time: 47.3 s
In [0]:
def elbow_plot(data, start_K, end_K, step):
    '''
    Generates an elbow plot to find optimal number of clusters
    graphing K values from start_K to end_K every step value
    
    INPUT: 
        data: Demographics DataFrame
        start_K: Inclusive starting value for cluster number
        end_K: Exclusive stopping value for cluster number
        step: Step value between start_K and end_K
    OUTPUT: Trimmed and cleaned demographics DataFrame
    '''
    score_list = []

    for i in range(start_K, end_K, step):
        print(i)
        start = time.time()
        kmeans = KMeans(i)
        model = kmeans.fit(data)
        score = model.score(data)
        score_list.append(abs(score))
        end = time.time()
        elapsed_time = end - start
        print(elapsed_time)

    plt.plot(range(start_K, end_K, step), 
    score_list, linestyle='--', marker='o', color='b');
    plt.xlabel('# of clusters K');
    plt.ylabel('Sum of squared errors');
    plt.title('SSE vs. K');
In [0]:
%%time
elbow_plot(azdias, 1, 20, 2)
1
16.719720125198364
3
113.28463459014893
5
187.56377291679382
7
443.5559284687042
9
435.1332473754883
11
514.429468870163
13
644.1196641921997
15
668.5473103523254
17
761.4800519943237
19
844.9050009250641
CPU times: user 1h 24min 8s, sys: 5min 24s, total: 1h 29min 32s
Wall time: 1h 17min 9s
In [0]:
#k=7 is taken as the curve starts to flatten after that 
%%time
k_m = KMeans(7)
k_model = k_m.fit(azdias_pca)
k_pred = k_model.predict(azdias_pca)
CPU times: user 3min 35s, sys: 17 s, total: 3min 52s
Wall time: 3min 20s
In [0]:
k_pred[:15]
Out[0]:
array([2, 1, 3, 1, 2, 2, 2, 1, 1, 6, 2, 2, 1, 6, 2], dtype=int32)
In [0]:
azdias_clustered = pd.DataFrame(k_model.predict(azdias_pca),columns=['Cluster'])
customers_clustered = pd.DataFrame(k_model.predict(customers_pca),columns=['Cluster'])
In [0]:
azdias_clustered.head()
Out[0]:
Cluster
0 2
1 1
2 3
3 1
4 2
In [0]:
# Finding difference in proportions of customer ratio to population ratio for each cluster.
customer_clusters = customers_clustered['Cluster'].value_counts().sort_index()
population_clusters = azdias_clustered["Cluster"].value_counts().sort_index()
df_stacked = pd.concat([customer_clusters,population_clusters],axis=1).reset_index()
df_stacked.columns = ['Cluster','Customer','Population']
df_stacked['Difference'] = (df_stacked['Customer']/df_stacked['Customer'].sum()*100) - (df_stacked['Population']/df_stacked['Population'].sum()*100)
df_stacked
Out[0]:
Cluster Customer Population Difference
0 0 11007 57698 0.575814
1 1 17691 109405 -1.369292
2 2 19172 125248 -2.404062
3 3 28823 163161 -0.154085
4 4 15731 88825 -0.053135
5 5 7389 57006 -2.128953
6 6 29400 125087 5.533713
In [0]:
# plotting the ratio values of population and customers per cluster side by side. 
df_stacked['Population_Percent'] = (df_stacked['Population']/df_stacked['Population'].sum()*100).round(2)
df_stacked['Customer_Percent'] = (df_stacked['Customer']/df_stacked['Customer'].sum()*100).round(2)


fig = plt.figure()

ax = fig.add_subplot(111)
ax = df_stacked['Population_Percent'].plot(x=df_stacked['Cluster'],width=-0.35,align='edge',color='blue',kind='bar',position=0)
ax = df_stacked['Customer_Percent'].plot(width = 0.35, align='edge',color='orange', kind='bar',position=1)
ax.set_xlabel('Clusters') 
ax.set_ylabel('Ratio %')
ax.xaxis.set(ticklabels=range(20))
ax.tick_params(axis = 'x', which = 'major', labelsize = 15)
ax.margins(x=0.5,y=0.1)
plt.legend(('Gen Population', 'Customer'),fontsize=15)
plt.title('Ratio of Population vs Customers as % of total per cluster', fontdict={'fontsize': 22})
plt.show()
In [0]:
# plotting the proportion difference calculated above.
fig = plt.figure()
ax = sns.barplot(x = df_stacked['Cluster'], y = df_stacked['Difference'])
ax.set(title='Difference in proportion between populations',ylabel='Proportion', xlabel='Clusters')
Out[0]:
[Text(0, 0.5, 'Proportion'),
 Text(0.5, 0, 'Clusters'),
 Text(0.5, 1.0, 'Difference in proportion between populations')]
In [0]:
target = customers_clustered[customers_clustered['Cluster'] == 6].index
general = azdias_clustered[azdias_clustered['Cluster'] == 2].index

df_target = customers.iloc[target]
df_general = azdias.iloc[general]

df_target_inv = pd.DataFrame(scaler.inverse_transform(df_target),columns=df_target.columns)
df_general_inv = pd.DataFrame(scaler.inverse_transform(df_general),columns=df_general.columns) 
In [0]:
df_target_inv.shape
Out[0]:
(29400, 229)
In [0]:
# plotting graph for comparison of each feature on over-represented vs under-represented cluster.
def plot_comparison(column, customer, general, clusters=[6,2]):
    '''
    Plots the distribution for a feature.
    
    Args:
        column (string) - feature to plot.
        customer (dataframe) - customer population cluster.
        general (dataframe) - general population cluster.
        clusters (list) - ints to use when pritting out title.
    '''   
    sns.set(style="darkgrid")
    fig, (ax1, ax2) = plt.subplots(figsize=(20,4), ncols=2)
    sns.countplot(x=column, data=df_target_inv, ax=ax1, palette="Set2")
    ax1.set_xlabel('Value')
    ax1.set_title(f'Distribution Customer population (cluster: {clusters[0]})')
    sns.countplot(x=column, data=df_general_inv, ax=ax2, palette="Set2")
    ax2.set_xlabel('Value')
    ax2.set_title(f'Distribution General population (cluster: {clusters[1]})')
    fig.suptitle(f'Feature: {column}')
In [0]:
for values in df_target_inv.columns:
  plot_comparison(column=values, customer=df_target_inv, general=df_general_inv)
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  if sys.path[0] == '':

The following conclusion can me made from the graphs above:

Part 2 Supervised Learning Model

In [0]:
azdias = pd.read_csv('/content/gdrive/My Drive/Bertelsmann Data Science Project/azdias_with_missing_values.csv')
nans = pd.read_csv('/content/gdrive/My Drive/Bertelsmann Data Science Project/nans.csv')
del nans['Unnamed: 0']
/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (9) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)
In [0]:
mailout_train = pd.read_csv('/content/gdrive/My Drive/Bertelsmann Data Science Project/Train.csv',dtype={18:'str',19:'str'})
mailout_test = pd.read_csv('/content/gdrive/My Drive/Bertelsmann Data Science Project/Test.csv',dtype={18:'str',19:'str'})
In [0]:
mailout_train = mailout_train.set_index('LNR')
mailout_test = mailout_test.set_index('LNR')
In [0]:
y = mailout_train['RESPONSE']
del mailout_train['RESPONSE']
X = mailout_train
In [0]:
plt.title('Distribution of customers responses',fontdict = {'fontsize' : 20})
sns.countplot(y);

Benchmark Model

In [0]:
cols_to_drop = ['CAMEO_DEU_2015', 'CAMEO_DEUG_2015', 'CAMEO_INTL_2015', 'D19_LETZTER_KAUF_BRANCHE','EINGEFUEGT_AM', 'OST_WEST_KZ']
X.drop(columns=cols_to_drop,inplace=True)
In [0]:
# run simple xgb model with default settings as benchmark
model = xgb.XGBClassifier()
model.fit(X,y)
Out[0]:
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=1, gamma=0,
              learning_rate=0.1, max_delta_step=0, max_depth=3,
              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,
              nthread=None, objective='binary:logistic', random_state=0,
              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
              silent=None, subsample=1, verbosity=1)
In [0]:
test_pred = model.predict_proba(mailout_test)
In [0]:
submission = pd.DataFrame({'LNR':mailout_test.index, 'RESPONSE':test_pred[:, 1]})
In [0]:
# Submitting to kaggle competitions page gave a score of 0.79772
%cd /content/gdrive/My Drive/Bertelsmann Data Science Project
submission.to_csv('submission_benchmark.csv', index=False)
/content/gdrive/My Drive/Bertelsmann Data Science Project

Data Cleaning

In [0]:
# Making cleaning function for new data sets based on pre-processing done before.
def cleaning_func(df):
  for row in nans['Attributes']:
    if row in df.columns:
        _m = nans.loc[nans['Attributes'] == row, 'missing_or_unknown'].iloc[0]
        _i = df.loc[:, row].isin(_m)
        df.loc[_i, row] = np.NaN
    else:
        continue

  for col in df.columns:
      if df[col].dtype == np.int64:
          df[col] = df[col].astype(np.float64)
  cols_to_drop = ['CAMEO_DEU_2015', 'CAMEO_DEUG_2015', 'CAMEO_INTL_2015', 'D19_LETZTER_KAUF_BRANCHE','EINGEFUEGT_AM']

  df.drop(columns=cols_to_drop,inplace=True)

  df['OST_WEST_KZ'].replace(['W', 'O'], [1, 0], inplace=True)

  dummy_cols = ['CJT_GESAMTTYP', 'FINANZTYP', 'GEBAEUDETYP', 'GEBAEUDETYP_RASTER', 'HEALTH_TYP', 
                    'KBA05_HERSTTEMP', 'KBA05_MAXHERST', 'KBA05_MODTEMP', 'LP_FAMILIE_GROB', 'LP_STATUS_GROB',
                    'NATIONALITAET_KZ', 'SHOPPER_TYP', 'VERS_TYP']
  for col in dummy_cols:
    dummy = pd.get_dummies(df[col], prefix = col)
    df = pd.concat([df, dummy], axis = 1)
  print(df.shape)
  df.drop(dummy_cols, axis=1, inplace = True)
  print(df.shape)

  imputer = SimpleImputer(missing_values=np.nan,strategy='mean')
  df = pd.DataFrame(imputer.fit_transform(df), columns = df.columns,index = df.index)
  print(df.head())
# not scaling values for xgboost as it results in better score without it.
#  scaler = StandardScaler()
#  df = pd.DataFrame(scaler.fit_transform(df), columns = df.columns,index = df.index)

  return df
In [0]:
X_cleaned = cleaning_func(X)
(42962, 422)
(42962, 409)
      AGER_TYP  AKT_DAT_KL  ...  VERS_TYP_1.0  VERS_TYP_2.0
LNR                         ...                            
1763       2.0         1.0  ...           0.0           1.0
1771       1.0         4.0  ...           1.0           0.0
1776       1.0         1.0  ...           1.0           0.0
1460       2.0         1.0  ...           0.0           1.0
1783       2.0         1.0  ...           1.0           0.0

[5 rows x 409 columns]
In [0]:
mailout_test_cleaned = cleaning_func(mailout_test)
(42833, 422)
(42833, 409)
      AGER_TYP  AKT_DAT_KL  ...  VERS_TYP_1.0  VERS_TYP_2.0
LNR                         ...                            
1754  2.000000         1.0  ...           1.0           0.0
1770  1.715618         1.0  ...           1.0           0.0
1465  2.000000         9.0  ...           1.0           0.0
1470  1.715618         7.0  ...           0.0           1.0
1478  1.000000         1.0  ...           1.0           0.0

[5 rows x 409 columns]

Model Selection

In [0]:
# Choosing the algorithm with best base performance.
def fit_classifier(model, params, X=X_cleaned, y=y):
    """
    Fits a classifier to its training data using GridSearchCV and calculates ROC AUC score
    
    INPUT:
    - clf (classifier): classifier to fit
    - params (dict): classifier parameters used with GridSearchCV
    - X_train (DataFrame): training input
    - y_train (DataFrame): training output
            
    OUTPUT:
    - classifier: input classifier fitted to the training data
    """
    
    # cv uses StratifiedKFold
    start = time.time()
    grid = GridSearchCV(estimator=model, param_grid=params, scoring='roc_auc', cv=5, verbose=0)
    print("Training {} :".format(model.__class__.__name__))
    grid.fit(X, y)
    end = time.time()
    time_taken = round(end-start,2)

    print(model.__class__.__name__)
    print("Time taken : {} secs".format(time_taken))
    print("Best score : {}".format(round(grid.best_score_,4)))
    print("*"*40)
    
    return grid.best_score_, grid.best_estimator_, time_taken
In [0]:
abc = AdaBoostClassifier(random_state=42) # AdaBoostClassifier
gbc = GradientBoostingClassifier(random_state=42) # GradientBoostingClassifier
xgbc = xgb.XGBClassifier(random_state=42) # XGBoost Classifier
cbc = CatBoostClassifier(random_state=42) # CatBoost Classifier
lgbm = lgb.LGBMClassifier(random_state=42) # Light GMB
In [0]:
model_names = []
model_scores = []
model_best_ests = []
model_time_taken = []
model_dict = {}

for model in [abc, gbc,xgbc,cbc,lgbm]:
    best_score, best_est, time_taken = fit_classifier(model, {})
    model_names.append(model.__class__.__name__)
    model_scores.append(best_score)
    model_best_ests.append(best_est)
    model_time_taken.append(time_taken)
Training AdaBoostClassifier :
AdaBoostClassifier
Time taken : 94.31 secs
Best score : 0.7259
****************************************
Training GradientBoostingClassifier :
GradientBoostingClassifier
Time taken : 405.97 secs
Best score : 0.7531
****************************************
Training XGBClassifier :
XGBClassifier
Time taken : 186.93 secs
Best score : 0.7574
****************************************
Training CatBoostClassifier :
Learning rate set to 0.046653
0:	learn: 0.5984964	total: 118ms	remaining: 1m 57s
1:	learn: 0.5174848	total: 192ms	remaining: 1m 35s
2:	learn: 0.4491613	total: 255ms	remaining: 1m 24s
3:	learn: 0.3909536	total: 327ms	remaining: 1m 21s
4:	learn: 0.3425278	total: 384ms	remaining: 1m 16s
5:	learn: 0.3016333	total: 461ms	remaining: 1m 16s
6:	learn: 0.2667780	total: 539ms	remaining: 1m 16s
7:	learn: 0.2371073	total: 610ms	remaining: 1m 15s
8:	learn: 0.2127636	total: 674ms	remaining: 1m 14s
9:	learn: 0.1918296	total: 747ms	remaining: 1m 13s
10:	learn: 0.1736684	total: 823ms	remaining: 1m 13s
11:	learn: 0.1589656	total: 887ms	remaining: 1m 13s
12:	learn: 0.1464800	total: 957ms	remaining: 1m 12s
13:	learn: 0.1356543	total: 1.02s	remaining: 1m 11s
14:	learn: 0.1265649	total: 1.09s	remaining: 1m 11s
15:	learn: 0.1186430	total: 1.18s	remaining: 1m 12s
16:	learn: 0.1118666	total: 1.25s	remaining: 1m 12s
17:	learn: 0.1060364	total: 1.33s	remaining: 1m 12s
18:	learn: 0.1009878	total: 1.39s	remaining: 1m 11s
19:	learn: 0.0966201	total: 1.46s	remaining: 1m 11s
20:	learn: 0.0928343	total: 1.53s	remaining: 1m 11s
21:	learn: 0.0895557	total: 1.59s	remaining: 1m 10s
22:	learn: 0.0866036	total: 1.66s	remaining: 1m 10s
23:	learn: 0.0841621	total: 1.71s	remaining: 1m 9s
24:	learn: 0.0819547	total: 1.79s	remaining: 1m 9s
25:	learn: 0.0793791	total: 1.85s	remaining: 1m 9s
26:	learn: 0.0776978	total: 1.91s	remaining: 1m 8s
27:	learn: 0.0761618	total: 1.98s	remaining: 1m 8s
28:	learn: 0.0748483	total: 2.05s	remaining: 1m 8s
29:	learn: 0.0736837	total: 2.12s	remaining: 1m 8s
30:	learn: 0.0726307	total: 2.18s	remaining: 1m 8s
31:	learn: 0.0712383	total: 2.27s	remaining: 1m 8s
32:	learn: 0.0704461	total: 2.34s	remaining: 1m 8s
33:	learn: 0.0687846	total: 2.41s	remaining: 1m 8s
34:	learn: 0.0681557	total: 2.48s	remaining: 1m 8s
35:	learn: 0.0675920	total: 2.54s	remaining: 1m 7s
36:	learn: 0.0667045	total: 2.6s	remaining: 1m 7s
37:	learn: 0.0662683	total: 2.67s	remaining: 1m 7s
38:	learn: 0.0659095	total: 2.74s	remaining: 1m 7s
39:	learn: 0.0650565	total: 2.84s	remaining: 1m 8s
40:	learn: 0.0640577	total: 2.91s	remaining: 1m 8s
41:	learn: 0.0637515	total: 2.99s	remaining: 1m 8s
42:	learn: 0.0635534	total: 3.04s	remaining: 1m 7s
43:	learn: 0.0631774	total: 3.1s	remaining: 1m 7s
44:	learn: 0.0629651	total: 3.17s	remaining: 1m 7s
45:	learn: 0.0624451	total: 3.25s	remaining: 1m 7s
46:	learn: 0.0621148	total: 3.32s	remaining: 1m 7s
47:	learn: 0.0619723	total: 3.39s	remaining: 1m 7s
48:	learn: 0.0617678	total: 3.45s	remaining: 1m 6s
49:	learn: 0.0616325	total: 3.51s	remaining: 1m 6s
50:	learn: 0.0610854	total: 3.59s	remaining: 1m 6s
51:	learn: 0.0608318	total: 3.65s	remaining: 1m 6s
52:	learn: 0.0606334	total: 3.72s	remaining: 1m 6s
53:	learn: 0.0601937	total: 3.79s	remaining: 1m 6s
54:	learn: 0.0598357	total: 3.87s	remaining: 1m 6s
55:	learn: 0.0596541	total: 3.95s	remaining: 1m 6s
56:	learn: 0.0595793	total: 4.01s	remaining: 1m 6s
57:	learn: 0.0592934	total: 4.08s	remaining: 1m 6s
58:	learn: 0.0591749	total: 4.14s	remaining: 1m 6s
59:	learn: 0.0588748	total: 4.21s	remaining: 1m 5s
60:	learn: 0.0588379	total: 4.26s	remaining: 1m 5s
61:	learn: 0.0587821	total: 4.34s	remaining: 1m 5s
62:	learn: 0.0587263	total: 4.39s	remaining: 1m 5s
63:	learn: 0.0584783	total: 4.48s	remaining: 1m 5s
64:	learn: 0.0584529	total: 4.54s	remaining: 1m 5s
65:	learn: 0.0582038	total: 4.61s	remaining: 1m 5s
66:	learn: 0.0581779	total: 4.67s	remaining: 1m 4s
67:	learn: 0.0581424	total: 4.72s	remaining: 1m 4s
68:	learn: 0.0581291	total: 4.78s	remaining: 1m 4s
69:	learn: 0.0580628	total: 4.86s	remaining: 1m 4s
70:	learn: 0.0580487	total: 4.93s	remaining: 1m 4s
71:	learn: 0.0580325	total: 4.99s	remaining: 1m 4s
72:	learn: 0.0580070	total: 5.06s	remaining: 1m 4s
73:	learn: 0.0579805	total: 5.15s	remaining: 1m 4s
74:	learn: 0.0578142	total: 5.22s	remaining: 1m 4s
75:	learn: 0.0577535	total: 5.27s	remaining: 1m 4s
76:	learn: 0.0576352	total: 5.35s	remaining: 1m 4s
77:	learn: 0.0576062	total: 5.4s	remaining: 1m 3s
78:	learn: 0.0574128	total: 5.47s	remaining: 1m 3s
79:	learn: 0.0573974	total: 5.53s	remaining: 1m 3s
80:	learn: 0.0572207	total: 5.6s	remaining: 1m 3s
81:	learn: 0.0571952	total: 5.66s	remaining: 1m 3s
82:	learn: 0.0570486	total: 5.72s	remaining: 1m 3s
83:	learn: 0.0569989	total: 5.79s	remaining: 1m 3s
84:	learn: 0.0568666	total: 5.87s	remaining: 1m 3s
85:	learn: 0.0568474	total: 5.93s	remaining: 1m 2s
86:	learn: 0.0568348	total: 5.99s	remaining: 1m 2s
87:	learn: 0.0567896	total: 6.05s	remaining: 1m 2s
88:	learn: 0.0566719	total: 6.12s	remaining: 1m 2s
89:	learn: 0.0565802	total: 6.17s	remaining: 1m 2s
90:	learn: 0.0564764	total: 6.24s	remaining: 1m 2s
91:	learn: 0.0564566	total: 6.29s	remaining: 1m 2s
92:	learn: 0.0564356	total: 6.35s	remaining: 1m 1s
93:	learn: 0.0563110	total: 6.43s	remaining: 1m 1s
94:	learn: 0.0562388	total: 6.49s	remaining: 1m 1s
95:	learn: 0.0561737	total: 6.54s	remaining: 1m 1s
96:	learn: 0.0561080	total: 6.61s	remaining: 1m 1s
97:	learn: 0.0560331	total: 6.67s	remaining: 1m 1s
98:	learn: 0.0560194	total: 6.73s	remaining: 1m 1s
99:	learn: 0.0559532	total: 6.78s	remaining: 1m 1s
100:	learn: 0.0559100	total: 6.84s	remaining: 1m
101:	learn: 0.0558890	total: 6.91s	remaining: 1m
102:	learn: 0.0558777	total: 6.96s	remaining: 1m
103:	learn: 0.0558609	total: 7.01s	remaining: 1m
104:	learn: 0.0557094	total: 7.08s	remaining: 1m
105:	learn: 0.0556098	total: 7.15s	remaining: 1m
106:	learn: 0.0555945	total: 7.2s	remaining: 1m
107:	learn: 0.0555811	total: 7.25s	remaining: 59.9s
108:	learn: 0.0555121	total: 7.31s	remaining: 59.8s
109:	learn: 0.0554480	total: 7.38s	remaining: 59.7s
110:	learn: 0.0553628	total: 7.45s	remaining: 59.6s
111:	learn: 0.0553201	total: 7.51s	remaining: 59.5s
112:	learn: 0.0551729	total: 7.58s	remaining: 59.5s
113:	learn: 0.0551124	total: 7.64s	remaining: 59.4s
114:	learn: 0.0550905	total: 7.71s	remaining: 59.3s
115:	learn: 0.0550409	total: 7.76s	remaining: 59.2s
116:	learn: 0.0549446	total: 7.83s	remaining: 59.1s
117:	learn: 0.0548863	total: 7.89s	remaining: 59s
118:	learn: 0.0547979	total: 7.96s	remaining: 58.9s
119:	learn: 0.0547359	total: 8.01s	remaining: 58.7s
120:	learn: 0.0547240	total: 8.07s	remaining: 58.7s
121:	learn: 0.0546394	total: 8.14s	remaining: 58.6s
122:	learn: 0.0546220	total: 8.2s	remaining: 58.5s
123:	learn: 0.0545541	total: 8.26s	remaining: 58.3s
124:	learn: 0.0545073	total: 8.32s	remaining: 58.2s
125:	learn: 0.0544336	total: 8.37s	remaining: 58.1s
126:	learn: 0.0543658	total: 8.43s	remaining: 58s
127:	learn: 0.0543148	total: 8.51s	remaining: 58s
128:	learn: 0.0542711	total: 8.58s	remaining: 57.9s
129:	learn: 0.0542415	total: 8.64s	remaining: 57.8s
130:	learn: 0.0541950	total: 8.7s	remaining: 57.7s
131:	learn: 0.0541489	total: 8.76s	remaining: 57.6s
132:	learn: 0.0540546	total: 8.83s	remaining: 57.6s
133:	learn: 0.0539846	total: 8.9s	remaining: 57.6s
134:	learn: 0.0539636	total: 8.96s	remaining: 57.4s
135:	learn: 0.0539160	total: 9.02s	remaining: 57.3s
136:	learn: 0.0538743	total: 9.1s	remaining: 57.3s
137:	learn: 0.0538307	total: 9.16s	remaining: 57.2s
138:	learn: 0.0537968	total: 9.21s	remaining: 57.1s
139:	learn: 0.0537485	total: 9.28s	remaining: 57s
140:	learn: 0.0537200	total: 9.35s	remaining: 57s
141:	learn: 0.0536788	total: 9.42s	remaining: 56.9s
142:	learn: 0.0536304	total: 9.49s	remaining: 56.9s
143:	learn: 0.0535510	total: 9.56s	remaining: 56.8s
144:	learn: 0.0534845	total: 9.64s	remaining: 56.8s
145:	learn: 0.0534441	total: 9.69s	remaining: 56.7s
146:	learn: 0.0533874	total: 9.75s	remaining: 56.6s
147:	learn: 0.0533237	total: 9.82s	remaining: 56.5s
148:	learn: 0.0532896	total: 9.87s	remaining: 56.4s
149:	learn: 0.0532479	total: 9.95s	remaining: 56.4s
150:	learn: 0.0532024	total: 10s	remaining: 56.3s
151:	learn: 0.0531348	total: 10.1s	remaining: 56.3s
152:	learn: 0.0531106	total: 10.1s	remaining: 56.1s
153:	learn: 0.0530472	total: 10.2s	remaining: 56s
154:	learn: 0.0529459	total: 10.3s	remaining: 56s
155:	learn: 0.0528815	total: 10.3s	remaining: 55.9s
156:	learn: 0.0528233	total: 10.4s	remaining: 55.8s
157:	learn: 0.0527858	total: 10.5s	remaining: 55.7s
158:	learn: 0.0527230	total: 10.5s	remaining: 55.7s
159:	learn: 0.0526976	total: 10.6s	remaining: 55.5s
160:	learn: 0.0526183	total: 10.7s	remaining: 55.5s
161:	learn: 0.0525645	total: 10.7s	remaining: 55.4s
162:	learn: 0.0525393	total: 10.8s	remaining: 55.3s
163:	learn: 0.0524565	total: 10.8s	remaining: 55.2s
164:	learn: 0.0524296	total: 10.9s	remaining: 55s
165:	learn: 0.0523752	total: 10.9s	remaining: 55s
166:	learn: 0.0523368	total: 11s	remaining: 54.9s
167:	learn: 0.0522693	total: 11.1s	remaining: 54.8s
168:	learn: 0.0522458	total: 11.1s	remaining: 54.7s
169:	learn: 0.0522269	total: 11.2s	remaining: 54.6s
170:	learn: 0.0521956	total: 11.2s	remaining: 54.5s
171:	learn: 0.0521813	total: 11.3s	remaining: 54.4s
172:	learn: 0.0521522	total: 11.4s	remaining: 54.3s
173:	learn: 0.0520807	total: 11.4s	remaining: 54.2s
174:	learn: 0.0520461	total: 11.5s	remaining: 54.1s
175:	learn: 0.0519859	total: 11.5s	remaining: 54s
176:	learn: 0.0519487	total: 11.6s	remaining: 53.9s
177:	learn: 0.0518285	total: 11.7s	remaining: 53.9s
178:	learn: 0.0517880	total: 11.7s	remaining: 53.8s
179:	learn: 0.0517521	total: 11.8s	remaining: 53.8s
180:	learn: 0.0517177	total: 11.9s	remaining: 53.6s
181:	learn: 0.0516576	total: 11.9s	remaining: 53.6s
182:	learn: 0.0516330	total: 12s	remaining: 53.6s
183:	learn: 0.0515975	total: 12.1s	remaining: 53.4s
184:	learn: 0.0515708	total: 12.1s	remaining: 53.3s
185:	learn: 0.0515309	total: 12.2s	remaining: 53.2s
186:	learn: 0.0515052	total: 12.2s	remaining: 53.1s
187:	learn: 0.0514762	total: 12.3s	remaining: 53s
188:	learn: 0.0513932	total: 12.3s	remaining: 52.9s
189:	learn: 0.0513624	total: 12.4s	remaining: 52.9s
190:	learn: 0.0513372	total: 12.5s	remaining: 52.8s
191:	learn: 0.0513109	total: 12.5s	remaining: 52.7s
192:	learn: 0.0512557	total: 12.6s	remaining: 52.6s
193:	learn: 0.0512347	total: 12.6s	remaining: 52.5s
194:	learn: 0.0511479	total: 12.7s	remaining: 52.4s
195:	learn: 0.0511262	total: 12.8s	remaining: 52.3s
196:	learn: 0.0510875	total: 12.8s	remaining: 52.2s
197:	learn: 0.0510198	total: 12.9s	remaining: 52.1s
198:	learn: 0.0509614	total: 12.9s	remaining: 52.1s
199:	learn: 0.0509225	total: 13s	remaining: 52.1s
200:	learn: 0.0508864	total: 13.1s	remaining: 51.9s
201:	learn: 0.0508141	total: 13.1s	remaining: 51.8s
202:	learn: 0.0507152	total: 13.2s	remaining: 51.8s
203:	learn: 0.0506772	total: 13.2s	remaining: 51.7s
204:	learn: 0.0506275	total: 13.3s	remaining: 51.6s
205:	learn: 0.0505752	total: 13.4s	remaining: 51.5s
206:	learn: 0.0505347	total: 13.4s	remaining: 51.5s
207:	learn: 0.0505191	total: 13.5s	remaining: 51.4s
208:	learn: 0.0505000	total: 13.6s	remaining: 51.3s
209:	learn: 0.0504272	total: 13.6s	remaining: 51.2s
210:	learn: 0.0503942	total: 13.7s	remaining: 51.1s
211:	learn: 0.0503546	total: 13.7s	remaining: 51s
212:	learn: 0.0503357	total: 13.8s	remaining: 50.9s
213:	learn: 0.0502695	total: 13.8s	remaining: 50.9s
214:	learn: 0.0502041	total: 13.9s	remaining: 50.8s
215:	learn: 0.0501868	total: 14s	remaining: 50.7s
216:	learn: 0.0501237	total: 14s	remaining: 50.7s
217:	learn: 0.0500640	total: 14.1s	remaining: 50.6s
218:	learn: 0.0500539	total: 14.2s	remaining: 50.5s
219:	learn: 0.0499970	total: 14.2s	remaining: 50.4s
220:	learn: 0.0499314	total: 14.3s	remaining: 50.4s
221:	learn: 0.0499124	total: 14.4s	remaining: 50.3s
222:	learn: 0.0498703	total: 14.4s	remaining: 50.2s
223:	learn: 0.0498409	total: 14.5s	remaining: 50.1s
224:	learn: 0.0498032	total: 14.5s	remaining: 50.1s
225:	learn: 0.0497489	total: 14.6s	remaining: 50s
226:	learn: 0.0496981	total: 14.7s	remaining: 49.9s
227:	learn: 0.0496465	total: 14.7s	remaining: 49.8s
228:	learn: 0.0496020	total: 14.8s	remaining: 49.7s
229:	learn: 0.0495244	total: 14.8s	remaining: 49.7s
230:	learn: 0.0494785	total: 14.9s	remaining: 49.6s
231:	learn: 0.0494568	total: 15s	remaining: 49.5s
232:	learn: 0.0494259	total: 15s	remaining: 49.5s
233:	learn: 0.0494160	total: 15.1s	remaining: 49.4s
234:	learn: 0.0493682	total: 15.1s	remaining: 49.3s
235:	learn: 0.0493250	total: 15.2s	remaining: 49.2s
236:	learn: 0.0492914	total: 15.2s	remaining: 49.1s
237:	learn: 0.0492653	total: 15.3s	remaining: 49s
238:	learn: 0.0492396	total: 15.4s	remaining: 48.9s
239:	learn: 0.0491955	total: 15.4s	remaining: 48.8s
240:	learn: 0.0491487	total: 15.5s	remaining: 48.7s
241:	learn: 0.0491302	total: 15.5s	remaining: 48.6s
242:	learn: 0.0490581	total: 15.6s	remaining: 48.6s
243:	learn: 0.0489982	total: 15.6s	remaining: 48.5s
244:	learn: 0.0489104	total: 15.7s	remaining: 48.4s
245:	learn: 0.0488806	total: 15.8s	remaining: 48.3s
246:	learn: 0.0488145	total: 15.8s	remaining: 48.3s
247:	learn: 0.0487257	total: 15.9s	remaining: 48.2s
248:	learn: 0.0486843	total: 16s	remaining: 48.2s
249:	learn: 0.0485909	total: 16.1s	remaining: 48.2s
250:	learn: 0.0485026	total: 16.1s	remaining: 48.1s
251:	learn: 0.0484506	total: 16.2s	remaining: 48.1s
252:	learn: 0.0483668	total: 16.3s	remaining: 48s
253:	learn: 0.0483558	total: 16.3s	remaining: 47.9s
254:	learn: 0.0483262	total: 16.4s	remaining: 47.8s
255:	learn: 0.0483162	total: 16.4s	remaining: 47.7s
256:	learn: 0.0482644	total: 16.5s	remaining: 47.6s
257:	learn: 0.0481671	total: 16.5s	remaining: 47.6s
258:	learn: 0.0481241	total: 16.6s	remaining: 47.5s
259:	learn: 0.0480785	total: 16.7s	remaining: 47.4s
260:	learn: 0.0480742	total: 16.7s	remaining: 47.3s
261:	learn: 0.0480650	total: 16.8s	remaining: 47.2s
262:	learn: 0.0480305	total: 16.8s	remaining: 47.1s
263:	learn: 0.0480163	total: 16.9s	remaining: 47s
264:	learn: 0.0479586	total: 16.9s	remaining: 46.9s
265:	learn: 0.0479214	total: 17s	remaining: 46.8s
266:	learn: 0.0478576	total: 17.1s	remaining: 46.8s
267:	learn: 0.0478311	total: 17.1s	remaining: 46.7s
268:	learn: 0.0477673	total: 17.2s	remaining: 46.6s
269:	learn: 0.0476936	total: 17.2s	remaining: 46.6s
270:	learn: 0.0476626	total: 17.3s	remaining: 46.5s
271:	learn: 0.0476186	total: 17.3s	remaining: 46.4s
272:	learn: 0.0475718	total: 17.4s	remaining: 46.3s
273:	learn: 0.0474659	total: 17.5s	remaining: 46.3s
274:	learn: 0.0474180	total: 17.5s	remaining: 46.2s
275:	learn: 0.0473787	total: 17.6s	remaining: 46.1s
276:	learn: 0.0473421	total: 17.6s	remaining: 46s
277:	learn: 0.0473149	total: 17.7s	remaining: 46s
278:	learn: 0.0472643	total: 17.8s	remaining: 45.9s
279:	learn: 0.0472424	total: 17.8s	remaining: 45.8s
280:	learn: 0.0472020	total: 17.9s	remaining: 45.7s
281:	learn: 0.0471607	total: 17.9s	remaining: 45.6s
282:	learn: 0.0471286	total: 18s	remaining: 45.6s
283:	learn: 0.0471010	total: 18s	remaining: 45.5s
284:	learn: 0.0470664	total: 18.1s	remaining: 45.4s
285:	learn: 0.0470065	total: 18.2s	remaining: 45.4s
286:	learn: 0.0469235	total: 18.2s	remaining: 45.3s
287:	learn: 0.0469058	total: 18.3s	remaining: 45.2s
288:	learn: 0.0468337	total: 18.3s	remaining: 45.1s
289:	learn: 0.0467919	total: 18.4s	remaining: 45s
290:	learn: 0.0467783	total: 18.5s	remaining: 45s
291:	learn: 0.0467588	total: 18.5s	remaining: 44.9s
292:	learn: 0.0467037	total: 18.6s	remaining: 44.8s
293:	learn: 0.0466331	total: 18.6s	remaining: 44.7s
294:	learn: 0.0465386	total: 18.7s	remaining: 44.7s
295:	learn: 0.0465092	total: 18.8s	remaining: 44.6s
296:	learn: 0.0464315	total: 18.8s	remaining: 44.6s
297:	learn: 0.0463413	total: 18.9s	remaining: 44.5s
298:	learn: 0.0462402	total: 19s	remaining: 44.5s
299:	learn: 0.0461792	total: 19s	remaining: 44.4s
300:	learn: 0.0461678	total: 19.1s	remaining: 44.3s
301:	learn: 0.0461162	total: 19.2s	remaining: 44.3s
302:	learn: 0.0460952	total: 19.2s	remaining: 44.2s
303:	learn: 0.0460454	total: 19.3s	remaining: 44.1s
304:	learn: 0.0459600	total: 19.3s	remaining: 44.1s
305:	learn: 0.0458958	total: 19.4s	remaining: 44s
306:	learn: 0.0458795	total: 19.5s	remaining: 43.9s
307:	learn: 0.0458281	total: 19.5s	remaining: 43.9s
308:	learn: 0.0458225	total: 19.6s	remaining: 43.8s
309:	learn: 0.0458095	total: 19.6s	remaining: 43.7s
310:	learn: 0.0457512	total: 19.7s	remaining: 43.6s
311:	learn: 0.0457410	total: 19.7s	remaining: 43.5s
312:	learn: 0.0457251	total: 19.8s	remaining: 43.5s
313:	learn: 0.0457160	total: 19.8s	remaining: 43.4s
314:	learn: 0.0457083	total: 19.9s	remaining: 43.3s
315:	learn: 0.0456120	total: 20s	remaining: 43.2s
316:	learn: 0.0455799	total: 20s	remaining: 43.1s
317:	learn: 0.0455394	total: 20.1s	remaining: 43s
318:	learn: 0.0454646	total: 20.1s	remaining: 43s
319:	learn: 0.0453936	total: 20.2s	remaining: 42.9s
320:	learn: 0.0453843	total: 20.3s	remaining: 42.8s
321:	learn: 0.0453148	total: 20.3s	remaining: 42.8s
322:	learn: 0.0452783	total: 20.4s	remaining: 42.7s
323:	learn: 0.0452142	total: 20.4s	remaining: 42.6s
324:	learn: 0.0451479	total: 20.5s	remaining: 42.5s
325:	learn: 0.0451318	total: 20.5s	remaining: 42.5s
326:	learn: 0.0450500	total: 20.6s	remaining: 42.4s
327:	learn: 0.0449889	total: 20.7s	remaining: 42.4s
328:	learn: 0.0449240	total: 20.7s	remaining: 42.3s
329:	learn: 0.0448830	total: 20.8s	remaining: 42.2s
330:	learn: 0.0448484	total: 20.8s	remaining: 42.1s
331:	learn: 0.0447607	total: 20.9s	remaining: 42.1s
332:	learn: 0.0447452	total: 21s	remaining: 42s
333:	learn: 0.0447283	total: 21s	remaining: 41.9s
334:	learn: 0.0446619	total: 21.1s	remaining: 41.9s
335:	learn: 0.0446362	total: 21.2s	remaining: 41.8s
336:	learn: 0.0445396	total: 21.2s	remaining: 41.8s
337:	learn: 0.0444847	total: 21.3s	remaining: 41.7s
338:	learn: 0.0444147	total: 21.4s	remaining: 41.6s
339:	learn: 0.0444067	total: 21.4s	remaining: 41.5s
340:	learn: 0.0443629	total: 21.5s	remaining: 41.5s
341:	learn: 0.0442756	total: 21.5s	remaining: 41.4s
342:	learn: 0.0442179	total: 21.6s	remaining: 41.4s
343:	learn: 0.0441424	total: 21.7s	remaining: 41.3s
344:	learn: 0.0440648	total: 21.7s	remaining: 41.3s
345:	learn: 0.0440324	total: 21.8s	remaining: 41.2s
346:	learn: 0.0440091	total: 21.8s	remaining: 41.1s
347:	learn: 0.0439445	total: 21.9s	remaining: 41s
348:	learn: 0.0439021	total: 22s	remaining: 41s
349:	learn: 0.0438242	total: 22s	remaining: 40.9s
350:	learn: 0.0438060	total: 22.1s	remaining: 40.8s
351:	learn: 0.0437868	total: 22.1s	remaining: 40.8s
352:	learn: 0.0437529	total: 22.2s	remaining: 40.7s
353:	learn: 0.0437088	total: 22.3s	remaining: 40.6s
354:	learn: 0.0436634	total: 22.3s	remaining: 40.5s
355:	learn: 0.0435799	total: 22.4s	remaining: 40.5s
356:	learn: 0.0435409	total: 22.4s	remaining: 40.4s
357:	learn: 0.0434967	total: 22.5s	remaining: 40.4s
358:	learn: 0.0434427	total: 22.6s	remaining: 40.3s
359:	learn: 0.0434155	total: 22.6s	remaining: 40.2s
360:	learn: 0.0433885	total: 22.7s	remaining: 40.1s
361:	learn: 0.0433681	total: 22.7s	remaining: 40.1s
362:	learn: 0.0433437	total: 22.8s	remaining: 40s
363:	learn: 0.0432879	total: 22.8s	remaining: 39.9s
364:	learn: 0.0432395	total: 22.9s	remaining: 39.8s
365:	learn: 0.0431688	total: 23s	remaining: 39.8s
366:	learn: 0.0431318	total: 23s	remaining: 39.7s
367:	learn: 0.0430681	total: 23.1s	remaining: 39.6s
368:	learn: 0.0430156	total: 23.1s	remaining: 39.6s
369:	learn: 0.0429681	total: 23.2s	remaining: 39.5s
370:	learn: 0.0429197	total: 23.3s	remaining: 39.5s
371:	learn: 0.0428822	total: 23.3s	remaining: 39.4s
372:	learn: 0.0428635	total: 23.4s	remaining: 39.3s
373:	learn: 0.0427947	total: 23.4s	remaining: 39.2s
374:	learn: 0.0427266	total: 23.5s	remaining: 39.2s
375:	learn: 0.0426909	total: 23.6s	remaining: 39.1s
376:	learn: 0.0426596	total: 23.6s	remaining: 39s
377:	learn: 0.0426180	total: 23.7s	remaining: 38.9s
378:	learn: 0.0425787	total: 23.7s	remaining: 38.9s
379:	learn: 0.0424802	total: 23.8s	remaining: 38.8s
380:	learn: 0.0424624	total: 23.9s	remaining: 38.8s
381:	learn: 0.0423686	total: 23.9s	remaining: 38.7s
382:	learn: 0.0422909	total: 24s	remaining: 38.6s
383:	learn: 0.0422730	total: 24s	remaining: 38.6s
384:	learn: 0.0422470	total: 24.1s	remaining: 38.5s
385:	learn: 0.0422007	total: 24.2s	remaining: 38.4s
386:	learn: 0.0421747	total: 24.2s	remaining: 38.4s
387:	learn: 0.0421455	total: 24.3s	remaining: 38.3s
388:	learn: 0.0421126	total: 24.4s	remaining: 38.2s
389:	learn: 0.0420498	total: 24.4s	remaining: 38.2s
390:	learn: 0.0419964	total: 24.5s	remaining: 38.1s
391:	learn: 0.0419488	total: 24.5s	remaining: 38.1s
392:	learn: 0.0418817	total: 24.6s	remaining: 38s
393:	learn: 0.0418258	total: 24.7s	remaining: 37.9s
394:	learn: 0.0417811	total: 24.7s	remaining: 37.9s
395:	learn: 0.0417461	total: 24.8s	remaining: 37.8s
396:	learn: 0.0417093	total: 24.8s	remaining: 37.7s
397:	learn: 0.0416759	total: 24.9s	remaining: 37.7s
398:	learn: 0.0416136	total: 25s	remaining: 37.6s
399:	learn: 0.0415418	total: 25s	remaining: 37.5s
400:	learn: 0.0414867	total: 25.1s	remaining: 37.5s
401:	learn: 0.0414718	total: 25.2s	remaining: 37.4s
402:	learn: 0.0414075	total: 25.2s	remaining: 37.4s
403:	learn: 0.0413209	total: 25.3s	remaining: 37.3s
404:	learn: 0.0413119	total: 25.4s	remaining: 37.3s
405:	learn: 0.0412772	total: 25.4s	remaining: 37.2s
406:	learn: 0.0412465	total: 25.5s	remaining: 37.1s
407:	learn: 0.0412238	total: 25.5s	remaining: 37s
408:	learn: 0.0412064	total: 25.6s	remaining: 36.9s
409:	learn: 0.0411536	total: 25.6s	remaining: 36.9s
410:	learn: 0.0411094	total: 25.7s	remaining: 36.8s
411:	learn: 0.0410678	total: 25.8s	remaining: 36.8s
412:	learn: 0.0409482	total: 25.8s	remaining: 36.7s
413:	learn: 0.0409095	total: 25.9s	remaining: 36.6s
414:	learn: 0.0408622	total: 25.9s	remaining: 36.6s
415:	learn: 0.0408277	total: 26s	remaining: 36.5s
416:	learn: 0.0407425	total: 26.1s	remaining: 36.5s
417:	learn: 0.0407340	total: 26.1s	remaining: 36.4s
418:	learn: 0.0406378	total: 26.2s	remaining: 36.4s
419:	learn: 0.0406039	total: 26.3s	remaining: 36.3s
420:	learn: 0.0405936	total: 26.3s	remaining: 36.2s
421:	learn: 0.0405507	total: 26.4s	remaining: 36.2s
422:	learn: 0.0405427	total: 26.5s	remaining: 36.1s
423:	learn: 0.0405249	total: 26.5s	remaining: 36s
424:	learn: 0.0405115	total: 26.6s	remaining: 35.9s
425:	learn: 0.0404955	total: 26.6s	remaining: 35.9s
426:	learn: 0.0404590	total: 26.7s	remaining: 35.8s
427:	learn: 0.0404377	total: 26.7s	remaining: 35.7s
428:	learn: 0.0403633	total: 26.8s	remaining: 35.7s
429:	learn: 0.0402858	total: 26.9s	remaining: 35.6s
430:	learn: 0.0402079	total: 26.9s	remaining: 35.6s
431:	learn: 0.0401423	total: 27s	remaining: 35.5s
432:	learn: 0.0400764	total: 27.1s	remaining: 35.4s
433:	learn: 0.0400269	total: 27.1s	remaining: 35.4s
434:	learn: 0.0400086	total: 27.2s	remaining: 35.3s
435:	learn: 0.0399842	total: 27.3s	remaining: 35.3s
436:	learn: 0.0399375	total: 27.3s	remaining: 35.2s
437:	learn: 0.0399054	total: 27.4s	remaining: 35.1s
438:	learn: 0.0398743	total: 27.4s	remaining: 35.1s
439:	learn: 0.0398301	total: 27.5s	remaining: 35s
440:	learn: 0.0397548	total: 27.6s	remaining: 34.9s
441:	learn: 0.0397270	total: 27.6s	remaining: 34.9s
442:	learn: 0.0397146	total: 27.7s	remaining: 34.8s
443:	learn: 0.0396899	total: 27.7s	remaining: 34.7s
444:	learn: 0.0396464	total: 27.8s	remaining: 34.7s
445:	learn: 0.0395838	total: 27.9s	remaining: 34.6s
446:	learn: 0.0395410	total: 27.9s	remaining: 34.5s
447:	learn: 0.0394986	total: 28s	remaining: 34.5s
448:	learn: 0.0394499	total: 28s	remaining: 34.4s
449:	learn: 0.0394355	total: 28.1s	remaining: 34.3s
450:	learn: 0.0394040	total: 28.1s	remaining: 34.3s
451:	learn: 0.0393554	total: 28.2s	remaining: 34.2s
452:	learn: 0.0392753	total: 28.3s	remaining: 34.2s
453:	learn: 0.0392697	total: 28.4s	remaining: 34.1s
454:	learn: 0.0392386	total: 28.4s	remaining: 34s
455:	learn: 0.0392228	total: 28.5s	remaining: 33.9s
456:	learn: 0.0392004	total: 28.5s	remaining: 33.9s
457:	learn: 0.0391897	total: 28.6s	remaining: 33.8s
458:	learn: 0.0391403	total: 28.6s	remaining: 33.7s
459:	learn: 0.0390971	total: 28.7s	remaining: 33.7s
460:	learn: 0.0390710	total: 28.7s	remaining: 33.6s
461:	learn: 0.0390416	total: 28.8s	remaining: 33.5s
462:	learn: 0.0389849	total: 28.9s	remaining: 33.5s
463:	learn: 0.0389598	total: 28.9s	remaining: 33.4s
464:	learn: 0.0389121	total: 29s	remaining: 33.3s
465:	learn: 0.0388612	total: 29s	remaining: 33.3s
466:	learn: 0.0388297	total: 29.1s	remaining: 33.2s
467:	learn: 0.0387680	total: 29.1s	remaining: 33.1s
468:	learn: 0.0387395	total: 29.2s	remaining: 33.1s
469:	learn: 0.0387198	total: 29.3s	remaining: 33s
470:	learn: 0.0387100	total: 29.3s	remaining: 33s
471:	learn: 0.0386890	total: 29.4s	remaining: 32.9s
472:	learn: 0.0386809	total: 29.4s	remaining: 32.8s
473:	learn: 0.0386761	total: 29.5s	remaining: 32.7s
474:	learn: 0.0386322	total: 29.6s	remaining: 32.7s
475:	learn: 0.0385952	total: 29.6s	remaining: 32.6s
476:	learn: 0.0385782	total: 29.7s	remaining: 32.5s
477:	learn: 0.0385533	total: 29.7s	remaining: 32.5s
478:	learn: 0.0385440	total: 29.8s	remaining: 32.4s
479:	learn: 0.0384687	total: 29.8s	remaining: 32.3s
480:	learn: 0.0384146	total: 29.9s	remaining: 32.3s
481:	learn: 0.0383781	total: 30s	remaining: 32.2s
482:	learn: 0.0383351	total: 30s	remaining: 32.2s
483:	learn: 0.0382692	total: 30.1s	remaining: 32.1s
484:	learn: 0.0382577	total: 30.2s	remaining: 32s
485:	learn: 0.0382475	total: 30.2s	remaining: 32s
486:	learn: 0.0382354	total: 30.3s	remaining: 31.9s
487:	learn: 0.0381931	total: 30.3s	remaining: 31.8s
488:	learn: 0.0381758	total: 30.4s	remaining: 31.8s
489:	learn: 0.0381134	total: 30.5s	remaining: 31.7s
490:	learn: 0.0380616	total: 30.5s	remaining: 31.6s
491:	learn: 0.0380396	total: 30.6s	remaining: 31.6s
492:	learn: 0.0379847	total: 30.6s	remaining: 31.5s
493:	learn: 0.0379135	total: 30.7s	remaining: 31.5s
494:	learn: 0.0378873	total: 30.8s	remaining: 31.4s
495:	learn: 0.0378592	total: 30.8s	remaining: 31.3s
496:	learn: 0.0378069	total: 30.9s	remaining: 31.3s
497:	learn: 0.0377602	total: 30.9s	remaining: 31.2s
498:	learn: 0.0377395	total: 31s	remaining: 31.1s
499:	learn: 0.0376983	total: 31.1s	remaining: 31.1s
500:	learn: 0.0376906	total: 31.1s	remaining: 31s
501:	learn: 0.0376738	total: 31.2s	remaining: 30.9s
502:	learn: 0.0376269	total: 31.2s	remaining: 30.9s
503:	learn: 0.0375788	total: 31.3s	remaining: 30.8s
504:	learn: 0.0375720	total: 31.3s	remaining: 30.7s
505:	learn: 0.0375561	total: 31.4s	remaining: 30.7s
506:	learn: 0.0375070	total: 31.5s	remaining: 30.6s
507:	learn: 0.0374092	total: 31.5s	remaining: 30.5s
508:	learn: 0.0373481	total: 31.6s	remaining: 30.5s
509:	learn: 0.0373281	total: 31.6s	remaining: 30.4s
510:	learn: 0.0372564	total: 31.7s	remaining: 30.3s
511:	learn: 0.0371737	total: 31.8s	remaining: 30.3s
512:	learn: 0.0371465	total: 31.8s	remaining: 30.2s
513:	learn: 0.0371293	total: 31.9s	remaining: 30.2s
514:	learn: 0.0370749	total: 32s	remaining: 30.1s
515:	learn: 0.0369783	total: 32s	remaining: 30.1s
516:	learn: 0.0369438	total: 32.1s	remaining: 30s
517:	learn: 0.0368864	total: 32.2s	remaining: 29.9s
518:	learn: 0.0368767	total: 32.2s	remaining: 29.9s
519:	learn: 0.0368659	total: 32.3s	remaining: 29.8s
520:	learn: 0.0368447	total: 32.3s	remaining: 29.7s
521:	learn: 0.0367817	total: 32.4s	remaining: 29.7s
522:	learn: 0.0367368	total: 32.5s	remaining: 29.6s
523:	learn: 0.0367189	total: 32.5s	remaining: 29.5s
524:	learn: 0.0366582	total: 32.6s	remaining: 29.5s
525:	learn: 0.0365896	total: 32.7s	remaining: 29.4s
526:	learn: 0.0365583	total: 32.7s	remaining: 29.4s
527:	learn: 0.0365469	total: 32.8s	remaining: 29.3s
528:	learn: 0.0364960	total: 32.8s	remaining: 29.2s
529:	learn: 0.0364906	total: 32.9s	remaining: 29.1s
530:	learn: 0.0364392	total: 32.9s	remaining: 29.1s
531:	learn: 0.0364072	total: 33s	remaining: 29s
532:	learn: 0.0363625	total: 33.1s	remaining: 29s
533:	learn: 0.0363101	total: 33.1s	remaining: 28.9s
534:	learn: 0.0362770	total: 33.2s	remaining: 28.8s
535:	learn: 0.0362354	total: 33.2s	remaining: 28.8s
536:	learn: 0.0362286	total: 33.3s	remaining: 28.7s
537:	learn: 0.0361657	total: 33.4s	remaining: 28.7s
538:	learn: 0.0361234	total: 33.5s	remaining: 28.6s
539:	learn: 0.0360824	total: 33.5s	remaining: 28.5s
540:	learn: 0.0360119	total: 33.6s	remaining: 28.5s
541:	learn: 0.0359744	total: 33.6s	remaining: 28.4s
542:	learn: 0.0359640	total: 33.7s	remaining: 28.4s
543:	learn: 0.0358927	total: 33.8s	remaining: 28.3s
544:	learn: 0.0358785	total: 33.8s	remaining: 28.2s
545:	learn: 0.0358460	total: 33.9s	remaining: 28.2s
546:	learn: 0.0358318	total: 33.9s	remaining: 28.1s
547:	learn: 0.0358191	total: 34s	remaining: 28s
548:	learn: 0.0357661	total: 34s	remaining: 28s
549:	learn: 0.0357449	total: 34.1s	remaining: 27.9s
550:	learn: 0.0357398	total: 34.1s	remaining: 27.8s
551:	learn: 0.0356651	total: 34.2s	remaining: 27.8s
552:	learn: 0.0356146	total: 34.3s	remaining: 27.7s
553:	learn: 0.0356048	total: 34.3s	remaining: 27.6s
554:	learn: 0.0355632	total: 34.4s	remaining: 27.6s
555:	learn: 0.0355318	total: 34.5s	remaining: 27.5s
556:	learn: 0.0354705	total: 34.5s	remaining: 27.5s
557:	learn: 0.0354214	total: 34.6s	remaining: 27.4s
558:	learn: 0.0353972	total: 34.7s	remaining: 27.3s
559:	learn: 0.0353456	total: 34.7s	remaining: 27.3s
560:	learn: 0.0353001	total: 34.8s	remaining: 27.2s
561:	learn: 0.0352404	total: 34.8s	remaining: 27.2s
562:	learn: 0.0352108	total: 34.9s	remaining: 27.1s
563:	learn: 0.0351706	total: 35s	remaining: 27s
564:	learn: 0.0351596	total: 35s	remaining: 27s
565:	learn: 0.0351230	total: 35.1s	remaining: 26.9s
566:	learn: 0.0350659	total: 35.1s	remaining: 26.8s
567:	learn: 0.0350281	total: 35.2s	remaining: 26.8s
568:	learn: 0.0349911	total: 35.3s	remaining: 26.7s
569:	learn: 0.0349384	total: 35.3s	remaining: 26.6s
570:	learn: 0.0349022	total: 35.4s	remaining: 26.6s
571:	learn: 0.0348624	total: 35.5s	remaining: 26.5s
572:	learn: 0.0348173	total: 35.5s	remaining: 26.5s
573:	learn: 0.0347662	total: 35.6s	remaining: 26.4s
574:	learn: 0.0347601	total: 35.6s	remaining: 26.3s
575:	learn: 0.0347471	total: 35.7s	remaining: 26.3s
576:	learn: 0.0346810	total: 35.7s	remaining: 26.2s
577:	learn: 0.0346397	total: 35.8s	remaining: 26.1s
578:	learn: 0.0345821	total: 35.9s	remaining: 26.1s
579:	learn: 0.0345742	total: 35.9s	remaining: 26s
580:	learn: 0.0345290	total: 36s	remaining: 25.9s
581:	learn: 0.0345233	total: 36s	remaining: 25.9s
582:	learn: 0.0344813	total: 36.1s	remaining: 25.8s
583:	learn: 0.0344399	total: 36.1s	remaining: 25.7s
584:	learn: 0.0344009	total: 36.2s	remaining: 25.7s
585:	learn: 0.0343916	total: 36.3s	remaining: 25.6s
586:	learn: 0.0343775	total: 36.3s	remaining: 25.5s
587:	learn: 0.0343572	total: 36.4s	remaining: 25.5s
588:	learn: 0.0343183	total: 36.4s	remaining: 25.4s
589:	learn: 0.0342284	total: 36.5s	remaining: 25.4s
590:	learn: 0.0342185	total: 36.6s	remaining: 25.3s
591:	learn: 0.0341894	total: 36.6s	remaining: 25.2s
592:	learn: 0.0341371	total: 36.7s	remaining: 25.2s
593:	learn: 0.0340552	total: 36.8s	remaining: 25.1s
594:	learn: 0.0340151	total: 36.8s	remaining: 25.1s
595:	learn: 0.0339545	total: 36.9s	remaining: 25s
596:	learn: 0.0339198	total: 36.9s	remaining: 24.9s
597:	learn: 0.0338800	total: 37s	remaining: 24.9s
598:	learn: 0.0338456	total: 37.1s	remaining: 24.8s
599:	learn: 0.0338237	total: 37.1s	remaining: 24.7s
600:	learn: 0.0337975	total: 37.2s	remaining: 24.7s
601:	learn: 0.0337396	total: 37.2s	remaining: 24.6s
602:	learn: 0.0337147	total: 37.3s	remaining: 24.5s
603:	learn: 0.0337071	total: 37.3s	remaining: 24.5s
604:	learn: 0.0336616	total: 37.4s	remaining: 24.4s
605:	learn: 0.0336337	total: 37.5s	remaining: 24.4s
606:	learn: 0.0336211	total: 37.5s	remaining: 24.3s
607:	learn: 0.0335696	total: 37.6s	remaining: 24.2s
608:	learn: 0.0335476	total: 37.6s	remaining: 24.2s
609:	learn: 0.0335383	total: 37.7s	remaining: 24.1s
610:	learn: 0.0334853	total: 37.8s	remaining: 24s
611:	learn: 0.0334643	total: 37.8s	remaining: 24s
612:	learn: 0.0334138	total: 37.9s	remaining: 23.9s
613:	learn: 0.0333841	total: 38s	remaining: 23.9s
614:	learn: 0.0333575	total: 38s	remaining: 23.8s
615:	learn: 0.0333045	total: 38.1s	remaining: 23.8s
616:	learn: 0.0332771	total: 38.2s	remaining: 23.7s
617:	learn: 0.0332704	total: 38.2s	remaining: 23.6s
618:	learn: 0.0332618	total: 38.3s	remaining: 23.6s
619:	learn: 0.0332470	total: 38.3s	remaining: 23.5s
620:	learn: 0.0332375	total: 38.4s	remaining: 23.4s
621:	learn: 0.0332294	total: 38.4s	remaining: 23.4s
622:	learn: 0.0331641	total: 38.5s	remaining: 23.3s
623:	learn: 0.0331218	total: 38.6s	remaining: 23.2s
624:	learn: 0.0331115	total: 38.6s	remaining: 23.2s
625:	learn: 0.0330777	total: 38.7s	remaining: 23.1s
626:	learn: 0.0330638	total: 38.7s	remaining: 23s
627:	learn: 0.0330220	total: 38.8s	remaining: 23s
628:	learn: 0.0329818	total: 38.8s	remaining: 22.9s
629:	learn: 0.0329710	total: 38.9s	remaining: 22.8s
630:	learn: 0.0329632	total: 39s	remaining: 22.8s
631:	learn: 0.0329174	total: 39s	remaining: 22.7s
632:	learn: 0.0329080	total: 39.1s	remaining: 22.7s
633:	learn: 0.0328627	total: 39.1s	remaining: 22.6s
634:	learn: 0.0328036	total: 39.2s	remaining: 22.5s
635:	learn: 0.0327840	total: 39.3s	remaining: 22.5s
636:	learn: 0.0327447	total: 39.3s	remaining: 22.4s
637:	learn: 0.0327121	total: 39.4s	remaining: 22.3s
638:	learn: 0.0326893	total: 39.5s	remaining: 22.3s
639:	learn: 0.0326511	total: 39.5s	remaining: 22.2s
640:	learn: 0.0326399	total: 39.6s	remaining: 22.2s
641:	learn: 0.0326315	total: 39.6s	remaining: 22.1s
642:	learn: 0.0326189	total: 39.7s	remaining: 22s
643:	learn: 0.0325863	total: 39.8s	remaining: 22s
644:	learn: 0.0325768	total: 39.8s	remaining: 21.9s
645:	learn: 0.0325405	total: 39.9s	remaining: 21.9s
646:	learn: 0.0325047	total: 39.9s	remaining: 21.8s
647:	learn: 0.0324503	total: 40s	remaining: 21.7s
648:	learn: 0.0324323	total: 40.1s	remaining: 21.7s
649:	learn: 0.0323707	total: 40.1s	remaining: 21.6s
650:	learn: 0.0323594	total: 40.2s	remaining: 21.5s
651:	learn: 0.0323063	total: 40.2s	remaining: 21.5s
652:	learn: 0.0322589	total: 40.3s	remaining: 21.4s
653:	learn: 0.0322144	total: 40.4s	remaining: 21.4s
654:	learn: 0.0321745	total: 40.4s	remaining: 21.3s
655:	learn: 0.0321318	total: 40.5s	remaining: 21.2s
656:	learn: 0.0321246	total: 40.5s	remaining: 21.2s
657:	learn: 0.0320909	total: 40.6s	remaining: 21.1s
658:	learn: 0.0320758	total: 40.7s	remaining: 21s
659:	learn: 0.0320252	total: 40.7s	remaining: 21s
660:	learn: 0.0319943	total: 40.8s	remaining: 20.9s
661:	learn: 0.0319378	total: 40.8s	remaining: 20.9s
662:	learn: 0.0319062	total: 40.9s	remaining: 20.8s
663:	learn: 0.0318854	total: 41s	remaining: 20.7s
664:	learn: 0.0318680	total: 41s	remaining: 20.7s
665:	learn: 0.0318217	total: 41.1s	remaining: 20.6s
666:	learn: 0.0317511	total: 41.1s	remaining: 20.5s
667:	learn: 0.0317047	total: 41.2s	remaining: 20.5s
668:	learn: 0.0316951	total: 41.3s	remaining: 20.4s
669:	learn: 0.0316746	total: 41.3s	remaining: 20.4s
670:	learn: 0.0316293	total: 41.4s	remaining: 20.3s
671:	learn: 0.0315927	total: 41.5s	remaining: 20.2s
672:	learn: 0.0315355	total: 41.5s	remaining: 20.2s
673:	learn: 0.0314930	total: 41.6s	remaining: 20.1s
674:	learn: 0.0314449	total: 41.7s	remaining: 20.1s
675:	learn: 0.0314218	total: 41.7s	remaining: 20s
676:	learn: 0.0313991	total: 41.8s	remaining: 19.9s
677:	learn: 0.0313651	total: 41.9s	remaining: 19.9s
678:	learn: 0.0313222	total: 41.9s	remaining: 19.8s
679:	learn: 0.0312550	total: 42s	remaining: 19.8s
680:	learn: 0.0312219	total: 42.1s	remaining: 19.7s
681:	learn: 0.0311820	total: 42.1s	remaining: 19.6s
682:	learn: 0.0311725	total: 42.2s	remaining: 19.6s
683:	learn: 0.0311650	total: 42.2s	remaining: 19.5s
684:	learn: 0.0311397	total: 42.3s	remaining: 19.4s
685:	learn: 0.0311027	total: 42.4s	remaining: 19.4s
686:	learn: 0.0310766	total: 42.4s	remaining: 19.3s
687:	learn: 0.0310387	total: 42.5s	remaining: 19.3s
688:	learn: 0.0309838	total: 42.6s	remaining: 19.2s
689:	learn: 0.0309137	total: 42.6s	remaining: 19.2s
690:	learn: 0.0308636	total: 42.7s	remaining: 19.1s
691:	learn: 0.0307775	total: 42.8s	remaining: 19s
692:	learn: 0.0307245	total: 42.8s	remaining: 19s
693:	learn: 0.0306641	total: 42.9s	remaining: 18.9s
694:	learn: 0.0306176	total: 43s	remaining: 18.9s
695:	learn: 0.0305495	total: 43.1s	remaining: 18.8s
696:	learn: 0.0305234	total: 43.1s	remaining: 18.7s
697:	learn: 0.0304816	total: 43.2s	remaining: 18.7s
698:	learn: 0.0304712	total: 43.2s	remaining: 18.6s
699:	learn: 0.0304198	total: 43.3s	remaining: 18.6s
700:	learn: 0.0303620	total: 43.4s	remaining: 18.5s
701:	learn: 0.0303541	total: 43.4s	remaining: 18.4s
702:	learn: 0.0303183	total: 43.5s	remaining: 18.4s
703:	learn: 0.0303093	total: 43.5s	remaining: 18.3s
704:	learn: 0.0302395	total: 43.6s	remaining: 18.3s
705:	learn: 0.0302353	total: 43.7s	remaining: 18.2s
706:	learn: 0.0302278	total: 43.7s	remaining: 18.1s
707:	learn: 0.0301771	total: 43.8s	remaining: 18.1s
708:	learn: 0.0301492	total: 43.8s	remaining: 18s
709:	learn: 0.0301414	total: 43.9s	remaining: 17.9s
710:	learn: 0.0301164	total: 44s	remaining: 17.9s
711:	learn: 0.0301013	total: 44s	remaining: 17.8s
712:	learn: 0.0300967	total: 44.1s	remaining: 17.7s
713:	learn: 0.0300659	total: 44.1s	remaining: 17.7s
714:	learn: 0.0300314	total: 44.2s	remaining: 17.6s
715:	learn: 0.0299846	total: 44.3s	remaining: 17.6s
716:	learn: 0.0299454	total: 44.3s	remaining: 17.5s
717:	learn: 0.0298854	total: 44.4s	remaining: 17.4s
718:	learn: 0.0298670	total: 44.5s	remaining: 17.4s
719:	learn: 0.0298329	total: 44.5s	remaining: 17.3s
720:	learn: 0.0298086	total: 44.6s	remaining: 17.3s
721:	learn: 0.0297697	total: 44.6s	remaining: 17.2s
722:	learn: 0.0297528	total: 44.7s	remaining: 17.1s
723:	learn: 0.0297120	total: 44.8s	remaining: 17.1s
724:	learn: 0.0296946	total: 44.8s	remaining: 17s
725:	learn: 0.0296381	total: 44.9s	remaining: 16.9s
726:	learn: 0.0295962	total: 45s	remaining: 16.9s
727:	learn: 0.0295896	total: 45s	remaining: 16.8s
728:	learn: 0.0295777	total: 45.1s	remaining: 16.8s
729:	learn: 0.0295415	total: 45.1s	remaining: 16.7s
730:	learn: 0.0294792	total: 45.2s	remaining: 16.6s
731:	learn: 0.0294513	total: 45.3s	remaining: 16.6s
732:	learn: 0.0294054	total: 45.3s	remaining: 16.5s
733:	learn: 0.0293472	total: 45.4s	remaining: 16.5s
734:	learn: 0.0293049	total: 45.5s	remaining: 16.4s
735:	learn: 0.0292788	total: 45.5s	remaining: 16.3s
736:	learn: 0.0292547	total: 45.6s	remaining: 16.3s
737:	learn: 0.0292121	total: 45.7s	remaining: 16.2s
738:	learn: 0.0291666	total: 45.8s	remaining: 16.2s
739:	learn: 0.0291286	total: 45.8s	remaining: 16.1s
740:	learn: 0.0291042	total: 45.9s	remaining: 16s
741:	learn: 0.0290656	total: 45.9s	remaining: 16s
742:	learn: 0.0290279	total: 46s	remaining: 15.9s
743:	learn: 0.0290045	total: 46s	remaining: 15.8s
744:	learn: 0.0289579	total: 46.1s	remaining: 15.8s
745:	learn: 0.0289329	total: 46.2s	remaining: 15.7s
746:	learn: 0.0289242	total: 46.2s	remaining: 15.7s
747:	learn: 0.0289183	total: 46.3s	remaining: 15.6s
748:	learn: 0.0289157	total: 46.3s	remaining: 15.5s
749:	learn: 0.0289088	total: 46.4s	remaining: 15.5s
750:	learn: 0.0288880	total: 46.4s	remaining: 15.4s
751:	learn: 0.0288812	total: 46.5s	remaining: 15.3s
752:	learn: 0.0288509	total: 46.5s	remaining: 15.3s
753:	learn: 0.0288170	total: 46.6s	remaining: 15.2s
754:	learn: 0.0287900	total: 46.7s	remaining: 15.1s
755:	learn: 0.0287767	total: 46.7s	remaining: 15.1s
756:	learn: 0.0287177	total: 46.8s	remaining: 15s
757:	learn: 0.0287095	total: 46.8s	remaining: 15s
758:	learn: 0.0286922	total: 46.9s	remaining: 14.9s
759:	learn: 0.0286834	total: 46.9s	remaining: 14.8s
760:	learn: 0.0286668	total: 47s	remaining: 14.8s
761:	learn: 0.0286496	total: 47.1s	remaining: 14.7s
762:	learn: 0.0286405	total: 47.1s	remaining: 14.6s
763:	learn: 0.0286325	total: 47.2s	remaining: 14.6s
764:	learn: 0.0285947	total: 47.2s	remaining: 14.5s
765:	learn: 0.0285913	total: 47.3s	remaining: 14.4s
766:	learn: 0.0285650	total: 47.3s	remaining: 14.4s
767:	learn: 0.0285434	total: 47.4s	remaining: 14.3s
768:	learn: 0.0285293	total: 47.5s	remaining: 14.3s
769:	learn: 0.0284792	total: 47.5s	remaining: 14.2s
770:	learn: 0.0284603	total: 47.6s	remaining: 14.1s
771:	learn: 0.0284572	total: 47.6s	remaining: 14.1s
772:	learn: 0.0284308	total: 47.7s	remaining: 14s
773:	learn: 0.0284148	total: 47.8s	remaining: 13.9s
774:	learn: 0.0283892	total: 47.8s	remaining: 13.9s
775:	learn: 0.0283631	total: 47.9s	remaining: 13.8s
776:	learn: 0.0283441	total: 47.9s	remaining: 13.8s
777:	learn: 0.0282983	total: 48s	remaining: 13.7s
778:	learn: 0.0282827	total: 48s	remaining: 13.6s
779:	learn: 0.0282282	total: 48.1s	remaining: 13.6s
780:	learn: 0.0282244	total: 48.2s	remaining: 13.5s
781:	learn: 0.0282136	total: 48.2s	remaining: 13.4s
782:	learn: 0.0282010	total: 48.3s	remaining: 13.4s
783:	learn: 0.0281825	total: 48.3s	remaining: 13.3s
784:	learn: 0.0281740	total: 48.4s	remaining: 13.3s
785:	learn: 0.0281133	total: 48.5s	remaining: 13.2s
786:	learn: 0.0280334	total: 48.6s	remaining: 13.1s
787:	learn: 0.0279877	total: 48.6s	remaining: 13.1s
788:	learn: 0.0279573	total: 48.7s	remaining: 13s
789:	learn: 0.0279411	total: 48.7s	remaining: 13s
790:	learn: 0.0279303	total: 48.8s	remaining: 12.9s
791:	learn: 0.0278966	total: 48.9s	remaining: 12.8s
792:	learn: 0.0278721	total: 48.9s	remaining: 12.8s
793:	learn: 0.0278671	total: 49s	remaining: 12.7s
794:	learn: 0.0278506	total: 49s	remaining: 12.6s
795:	learn: 0.0278275	total: 49.1s	remaining: 12.6s
796:	learn: 0.0278201	total: 49.2s	remaining: 12.5s
797:	learn: 0.0277928	total: 49.2s	remaining: 12.5s
798:	learn: 0.0277831	total: 49.3s	remaining: 12.4s
799:	learn: 0.0277512	total: 49.3s	remaining: 12.3s
800:	learn: 0.0277335	total: 49.4s	remaining: 12.3s
801:	learn: 0.0277265	total: 49.4s	remaining: 12.2s
802:	learn: 0.0277121	total: 49.5s	remaining: 12.1s
803:	learn: 0.0276584	total: 49.6s	remaining: 12.1s
804:	learn: 0.0276433	total: 49.6s	remaining: 12s
805:	learn: 0.0276051	total: 49.7s	remaining: 12s
806:	learn: 0.0275666	total: 49.8s	remaining: 11.9s
807:	learn: 0.0275582	total: 49.8s	remaining: 11.8s
808:	learn: 0.0275127	total: 49.9s	remaining: 11.8s
809:	learn: 0.0274581	total: 50s	remaining: 11.7s
810:	learn: 0.0274541	total: 50s	remaining: 11.7s
811:	learn: 0.0274430	total: 50.1s	remaining: 11.6s
812:	learn: 0.0274360	total: 50.1s	remaining: 11.5s
813:	learn: 0.0274265	total: 50.2s	remaining: 11.5s
814:	learn: 0.0273876	total: 50.3s	remaining: 11.4s
815:	learn: 0.0273616	total: 50.3s	remaining: 11.3s
816:	learn: 0.0273470	total: 50.4s	remaining: 11.3s
817:	learn: 0.0273268	total: 50.4s	remaining: 11.2s
818:	learn: 0.0272933	total: 50.5s	remaining: 11.2s
819:	learn: 0.0272502	total: 50.6s	remaining: 11.1s
820:	learn: 0.0272039	total: 50.6s	remaining: 11s
821:	learn: 0.0271980	total: 50.7s	remaining: 11s
822:	learn: 0.0271605	total: 50.8s	remaining: 10.9s
823:	learn: 0.0271391	total: 50.8s	remaining: 10.9s
824:	learn: 0.0271073	total: 50.9s	remaining: 10.8s
825:	learn: 0.0270616	total: 51s	remaining: 10.7s
826:	learn: 0.0270219	total: 51s	remaining: 10.7s
827:	learn: 0.0269990	total: 51.1s	remaining: 10.6s
828:	learn: 0.0269487	total: 51.2s	remaining: 10.6s
829:	learn: 0.0269156	total: 51.2s	remaining: 10.5s
830:	learn: 0.0269122	total: 51.3s	remaining: 10.4s
831:	learn: 0.0268763	total: 51.3s	remaining: 10.4s
832:	learn: 0.0268676	total: 51.4s	remaining: 10.3s
833:	learn: 0.0268434	total: 51.4s	remaining: 10.2s
834:	learn: 0.0268223	total: 51.5s	remaining: 10.2s
835:	learn: 0.0268048	total: 51.5s	remaining: 10.1s
836:	learn: 0.0267800	total: 51.6s	remaining: 10.1s
837:	learn: 0.0267438	total: 51.7s	remaining: 9.99s
838:	learn: 0.0267118	total: 51.7s	remaining: 9.93s
839:	learn: 0.0266985	total: 51.8s	remaining: 9.87s
840:	learn: 0.0266894	total: 51.9s	remaining: 9.8s
841:	learn: 0.0266640	total: 51.9s	remaining: 9.74s
842:	learn: 0.0266117	total: 52s	remaining: 9.68s
843:	learn: 0.0265659	total: 52s	remaining: 9.62s
844:	learn: 0.0265553	total: 52.1s	remaining: 9.56s
845:	learn: 0.0265263	total: 52.2s	remaining: 9.49s
846:	learn: 0.0265159	total: 52.2s	remaining: 9.43s
847:	learn: 0.0264726	total: 52.3s	remaining: 9.37s
848:	learn: 0.0264385	total: 52.4s	remaining: 9.31s
849:	learn: 0.0264321	total: 52.4s	remaining: 9.25s
850:	learn: 0.0264258	total: 52.5s	remaining: 9.18s
851:	learn: 0.0263886	total: 52.5s	remaining: 9.12s
852:	learn: 0.0263638	total: 52.6s	remaining: 9.06s
853:	learn: 0.0263575	total: 52.6s	remaining: 9s
854:	learn: 0.0263445	total: 52.7s	remaining: 8.94s
855:	learn: 0.0262916	total: 52.8s	remaining: 8.88s
856:	learn: 0.0262546	total: 52.8s	remaining: 8.82s
857:	learn: 0.0262083	total: 52.9s	remaining: 8.76s
858:	learn: 0.0262007	total: 53s	remaining: 8.69s
859:	learn: 0.0261969	total: 53s	remaining: 8.63s
860:	learn: 0.0261850	total: 53.1s	remaining: 8.57s
861:	learn: 0.0261638	total: 53.1s	remaining: 8.51s
862:	learn: 0.0261366	total: 53.2s	remaining: 8.44s
863:	learn: 0.0260949	total: 53.3s	remaining: 8.38s
864:	learn: 0.0260891	total: 53.3s	remaining: 8.32s
865:	learn: 0.0260788	total: 53.4s	remaining: 8.26s
866:	learn: 0.0260577	total: 53.4s	remaining: 8.2s
867:	learn: 0.0260243	total: 53.5s	remaining: 8.14s
868:	learn: 0.0260169	total: 53.6s	remaining: 8.07s
869:	learn: 0.0260055	total: 53.6s	remaining: 8.01s
870:	learn: 0.0260007	total: 53.7s	remaining: 7.95s
871:	learn: 0.0259763	total: 53.7s	remaining: 7.89s
872:	learn: 0.0259378	total: 53.8s	remaining: 7.83s
873:	learn: 0.0259007	total: 53.9s	remaining: 7.76s
874:	learn: 0.0258827	total: 53.9s	remaining: 7.7s
875:	learn: 0.0258631	total: 54s	remaining: 7.64s
876:	learn: 0.0258425	total: 54.1s	remaining: 7.58s
877:	learn: 0.0258254	total: 54.1s	remaining: 7.52s
878:	learn: 0.0257989	total: 54.2s	remaining: 7.46s
879:	learn: 0.0257796	total: 54.2s	remaining: 7.39s
880:	learn: 0.0257646	total: 54.3s	remaining: 7.33s
881:	learn: 0.0257321	total: 54.4s	remaining: 7.27s
882:	learn: 0.0257291	total: 54.4s	remaining: 7.21s
883:	learn: 0.0257143	total: 54.5s	remaining: 7.15s
884:	learn: 0.0256962	total: 54.5s	remaining: 7.09s
885:	learn: 0.0256724	total: 54.6s	remaining: 7.03s
886:	learn: 0.0256591	total: 54.7s	remaining: 6.96s
887:	learn: 0.0256494	total: 54.7s	remaining: 6.9s
888:	learn: 0.0256019	total: 54.8s	remaining: 6.84s
889:	learn: 0.0255909	total: 54.8s	remaining: 6.78s
890:	learn: 0.0255625	total: 54.9s	remaining: 6.72s
891:	learn: 0.0255099	total: 55s	remaining: 6.66s
892:	learn: 0.0254941	total: 55s	remaining: 6.59s
893:	learn: 0.0254794	total: 55.1s	remaining: 6.53s
894:	learn: 0.0254695	total: 55.1s	remaining: 6.47s
895:	learn: 0.0254332	total: 55.2s	remaining: 6.41s
896:	learn: 0.0254285	total: 55.3s	remaining: 6.34s
897:	learn: 0.0254221	total: 55.3s	remaining: 6.28s
898:	learn: 0.0253972	total: 55.4s	remaining: 6.22s
899:	learn: 0.0253803	total: 55.4s	remaining: 6.16s
900:	learn: 0.0253556	total: 55.5s	remaining: 6.1s
901:	learn: 0.0253162	total: 55.6s	remaining: 6.04s
902:	learn: 0.0253064	total: 55.6s	remaining: 5.97s
903:	learn: 0.0252693	total: 55.7s	remaining: 5.91s
904:	learn: 0.0252644	total: 55.8s	remaining: 5.85s
905:	learn: 0.0252390	total: 55.8s	remaining: 5.79s
906:	learn: 0.0252245	total: 55.9s	remaining: 5.73s
907:	learn: 0.0252218	total: 55.9s	remaining: 5.67s
908:	learn: 0.0252139	total: 56s	remaining: 5.6s
909:	learn: 0.0251893	total: 56s	remaining: 5.54s
910:	learn: 0.0251643	total: 56.1s	remaining: 5.48s
911:	learn: 0.0251313	total: 56.2s	remaining: 5.42s
912:	learn: 0.0251152	total: 56.2s	remaining: 5.36s
913:	learn: 0.0250966	total: 56.3s	remaining: 5.29s
914:	learn: 0.0250890	total: 56.3s	remaining: 5.23s
915:	learn: 0.0250796	total: 56.4s	remaining: 5.17s
916:	learn: 0.0250295	total: 56.5s	remaining: 5.11s
917:	learn: 0.0250125	total: 56.5s	remaining: 5.05s
918:	learn: 0.0249652	total: 56.6s	remaining: 4.99s
919:	learn: 0.0249618	total: 56.6s	remaining: 4.92s
920:	learn: 0.0249315	total: 56.7s	remaining: 4.86s
921:	learn: 0.0249230	total: 56.8s	remaining: 4.8s
922:	learn: 0.0248958	total: 56.9s	remaining: 4.74s
923:	learn: 0.0248890	total: 56.9s	remaining: 4.68s
924:	learn: 0.0248846	total: 57s	remaining: 4.62s
925:	learn: 0.0248702	total: 57s	remaining: 4.56s
926:	learn: 0.0248576	total: 57.1s	remaining: 4.5s
927:	learn: 0.0248315	total: 57.1s	remaining: 4.43s
928:	learn: 0.0248023	total: 57.2s	remaining: 4.37s
929:	learn: 0.0247713	total: 57.3s	remaining: 4.31s
930:	learn: 0.0247500	total: 57.3s	remaining: 4.25s
931:	learn: 0.0247391	total: 57.4s	remaining: 4.19s
932:	learn: 0.0247247	total: 57.4s	remaining: 4.13s
933:	learn: 0.0247043	total: 57.5s	remaining: 4.06s
934:	learn: 0.0246815	total: 57.6s	remaining: 4s
935:	learn: 0.0246625	total: 57.6s	remaining: 3.94s
936:	learn: 0.0246569	total: 57.7s	remaining: 3.88s
937:	learn: 0.0246487	total: 57.7s	remaining: 3.81s
938:	learn: 0.0245969	total: 57.8s	remaining: 3.75s
939:	learn: 0.0245894	total: 57.9s	remaining: 3.69s
940:	learn: 0.0245609	total: 57.9s	remaining: 3.63s
941:	learn: 0.0245351	total: 58s	remaining: 3.57s
942:	learn: 0.0245014	total: 58.1s	remaining: 3.51s
943:	learn: 0.0244774	total: 58.1s	remaining: 3.45s
944:	learn: 0.0244619	total: 58.2s	remaining: 3.39s
945:	learn: 0.0244557	total: 58.2s	remaining: 3.32s
946:	learn: 0.0244007	total: 58.3s	remaining: 3.26s
947:	learn: 0.0243926	total: 58.4s	remaining: 3.2s
948:	learn: 0.0243854	total: 58.4s	remaining: 3.14s
949:	learn: 0.0243652	total: 58.5s	remaining: 3.08s
950:	learn: 0.0243340	total: 58.6s	remaining: 3.02s
951:	learn: 0.0243206	total: 58.6s	remaining: 2.96s
952:	learn: 0.0242761	total: 58.7s	remaining: 2.89s
953:	learn: 0.0242690	total: 58.7s	remaining: 2.83s
954:	learn: 0.0242435	total: 58.8s	remaining: 2.77s
955:	learn: 0.0242321	total: 58.9s	remaining: 2.71s
956:	learn: 0.0242254	total: 58.9s	remaining: 2.65s
957:	learn: 0.0242024	total: 59s	remaining: 2.58s
958:	learn: 0.0241852	total: 59s	remaining: 2.52s
959:	learn: 0.0241794	total: 59.1s	remaining: 2.46s
960:	learn: 0.0241476	total: 59.2s	remaining: 2.4s
961:	learn: 0.0241190	total: 59.3s	remaining: 2.34s
962:	learn: 0.0241011	total: 59.3s	remaining: 2.28s
963:	learn: 0.0240646	total: 59.4s	remaining: 2.22s
964:	learn: 0.0240499	total: 59.4s	remaining: 2.15s
965:	learn: 0.0240363	total: 59.5s	remaining: 2.09s
966:	learn: 0.0240055	total: 59.6s	remaining: 2.03s
967:	learn: 0.0239816	total: 59.6s	remaining: 1.97s
968:	learn: 0.0239455	total: 59.7s	remaining: 1.91s
969:	learn: 0.0239203	total: 59.8s	remaining: 1.85s
970:	learn: 0.0239032	total: 59.8s	remaining: 1.79s
971:	learn: 0.0238993	total: 59.9s	remaining: 1.72s
972:	learn: 0.0238767	total: 59.9s	remaining: 1.66s
973:	learn: 0.0238354	total: 1m	remaining: 1.6s
974:	learn: 0.0238126	total: 1m	remaining: 1.54s
975:	learn: 0.0238068	total: 1m	remaining: 1.48s
976:	learn: 0.0237859	total: 1m	remaining: 1.42s
977:	learn: 0.0237602	total: 1m	remaining: 1.35s
978:	learn: 0.0237356	total: 1m	remaining: 1.29s
979:	learn: 0.0237142	total: 1m	remaining: 1.23s
980:	learn: 0.0236955	total: 1m	remaining: 1.17s
981:	learn: 0.0236688	total: 1m	remaining: 1.11s
982:	learn: 0.0236643	total: 1m	remaining: 1.05s
983:	learn: 0.0236577	total: 1m	remaining: 986ms
984:	learn: 0.0236543	total: 1m	remaining: 924ms
985:	learn: 0.0236244	total: 1m	remaining: 862ms
986:	learn: 0.0236136	total: 1m	remaining: 801ms
987:	learn: 0.0235876	total: 1m	remaining: 739ms
988:	learn: 0.0235599	total: 1m	remaining: 678ms
989:	learn: 0.0235407	total: 1m	remaining: 616ms
990:	learn: 0.0235330	total: 1m 1s	remaining: 554ms
991:	learn: 0.0235069	total: 1m 1s	remaining: 493ms
992:	learn: 0.0234883	total: 1m 1s	remaining: 431ms
993:	learn: 0.0234754	total: 1m 1s	remaining: 370ms
994:	learn: 0.0234487	total: 1m 1s	remaining: 308ms
995:	learn: 0.0234426	total: 1m 1s	remaining: 246ms
996:	learn: 0.0234395	total: 1m 1s	remaining: 185ms
997:	learn: 0.0234286	total: 1m 1s	remaining: 123ms
998:	learn: 0.0234199	total: 1m 1s	remaining: 61.6ms
999:	learn: 0.0234178	total: 1m 1s	remaining: 0us
Learning rate set to 0.046653
0:	learn: 0.5985682	total: 63.5ms	remaining: 1m 3s
1:	learn: 0.5174281	total: 131ms	remaining: 1m 5s
2:	learn: 0.4491974	total: 201ms	remaining: 1m 6s
3:	learn: 0.3910678	total: 268ms	remaining: 1m 6s
4:	learn: 0.3425713	total: 326ms	remaining: 1m 4s
5:	learn: 0.3016304	total: 396ms	remaining: 1m 5s
6:	learn: 0.2665935	total: 487ms	remaining: 1m 9s
7:	learn: 0.2368855	total: 560ms	remaining: 1m 9s
8:	learn: 0.2125979	total: 622ms	remaining: 1m 8s
9:	learn: 0.1918649	total: 693ms	remaining: 1m 8s
10:	learn: 0.1736601	total: 764ms	remaining: 1m 8s
11:	learn: 0.1589547	total: 829ms	remaining: 1m 8s
12:	learn: 0.1463895	total: 908ms	remaining: 1m 8s
13:	learn: 0.1355454	total: 970ms	remaining: 1m 8s
14:	learn: 0.1264446	total: 1.05s	remaining: 1m 8s
15:	learn: 0.1185872	total: 1.13s	remaining: 1m 9s
16:	learn: 0.1118114	total: 1.2s	remaining: 1m 9s
17:	learn: 0.1060091	total: 1.28s	remaining: 1m 9s
18:	learn: 0.1009144	total: 1.35s	remaining: 1m 9s
19:	learn: 0.0965682	total: 1.41s	remaining: 1m 9s
20:	learn: 0.0927926	total: 1.49s	remaining: 1m 9s
21:	learn: 0.0895001	total: 1.56s	remaining: 1m 9s
22:	learn: 0.0865308	total: 1.64s	remaining: 1m 9s
23:	learn: 0.0840667	total: 1.69s	remaining: 1m 8s
24:	learn: 0.0818768	total: 1.75s	remaining: 1m 8s
25:	learn: 0.0799704	total: 1.82s	remaining: 1m 8s
26:	learn: 0.0782558	total: 1.88s	remaining: 1m 7s
27:	learn: 0.0766891	total: 1.96s	remaining: 1m 7s
28:	learn: 0.0753467	total: 2.03s	remaining: 1m 7s
29:	learn: 0.0741381	total: 2.1s	remaining: 1m 8s
30:	learn: 0.0726384	total: 2.17s	remaining: 1m 7s
31:	learn: 0.0716328	total: 2.25s	remaining: 1m 8s
32:	learn: 0.0708314	total: 2.32s	remaining: 1m 8s
33:	learn: 0.0691956	total: 2.39s	remaining: 1m 7s
34:	learn: 0.0685714	total: 2.46s	remaining: 1m 7s
35:	learn: 0.0680290	total: 2.52s	remaining: 1m 7s
36:	learn: 0.0672133	total: 2.59s	remaining: 1m 7s
37:	learn: 0.0666827	total: 2.68s	remaining: 1m 7s
38:	learn: 0.0663061	total: 2.74s	remaining: 1m 7s
39:	learn: 0.0654494	total: 2.82s	remaining: 1m 7s
40:	learn: 0.0645063	total: 2.9s	remaining: 1m 7s
41:	learn: 0.0641985	total: 2.98s	remaining: 1m 7s
42:	learn: 0.0639469	total: 3.04s	remaining: 1m 7s
43:	learn: 0.0637296	total: 3.1s	remaining: 1m 7s
44:	learn: 0.0630839	total: 3.18s	remaining: 1m 7s
45:	learn: 0.0625491	total: 3.25s	remaining: 1m 7s
46:	learn: 0.0622232	total: 3.32s	remaining: 1m 7s
47:	learn: 0.0620909	total: 3.39s	remaining: 1m 7s
48:	learn: 0.0617572	total: 3.47s	remaining: 1m 7s
49:	learn: 0.0616325	total: 3.52s	remaining: 1m 6s
50:	learn: 0.0611625	total: 3.61s	remaining: 1m 7s
51:	learn: 0.0609328	total: 3.68s	remaining: 1m 7s
52:	learn: 0.0607317	total: 3.75s	remaining: 1m 6s
53:	learn: 0.0602855	total: 3.83s	remaining: 1m 7s
54:	learn: 0.0599535	total: 3.9s	remaining: 1m 7s
55:	learn: 0.0598174	total: 3.96s	remaining: 1m 6s
56:	learn: 0.0597557	total: 4.02s	remaining: 1m 6s
57:	learn: 0.0594558	total: 4.09s	remaining: 1m 6s
58:	learn: 0.0591851	total: 4.16s	remaining: 1m 6s
59:	learn: 0.0589914	total: 4.22s	remaining: 1m 6s
60:	learn: 0.0589544	total: 4.28s	remaining: 1m 5s
61:	learn: 0.0588911	total: 4.34s	remaining: 1m 5s
62:	learn: 0.0588599	total: 4.4s	remaining: 1m 5s
63:	learn: 0.0588313	total: 4.46s	remaining: 1m 5s
64:	learn: 0.0587915	total: 4.51s	remaining: 1m 4s
65:	learn: 0.0585534	total: 4.59s	remaining: 1m 4s
66:	learn: 0.0585050	total: 4.65s	remaining: 1m 4s
67:	learn: 0.0584648	total: 4.7s	remaining: 1m 4s
68:	learn: 0.0584267	total: 4.76s	remaining: 1m 4s
69:	learn: 0.0584056	total: 4.81s	remaining: 1m 3s
70:	learn: 0.0583819	total: 4.88s	remaining: 1m 3s
71:	learn: 0.0583587	total: 4.94s	remaining: 1m 3s
72:	learn: 0.0581741	total: 5.01s	remaining: 1m 3s
73:	learn: 0.0581403	total: 5.08s	remaining: 1m 3s
74:	learn: 0.0579057	total: 5.15s	remaining: 1m 3s
75:	learn: 0.0578953	total: 5.21s	remaining: 1m 3s
76:	learn: 0.0577752	total: 5.28s	remaining: 1m 3s
77:	learn: 0.0577553	total: 5.34s	remaining: 1m 3s
78:	learn: 0.0577058	total: 5.39s	remaining: 1m 2s
79:	learn: 0.0576870	total: 5.45s	remaining: 1m 2s
80:	learn: 0.0575105	total: 5.53s	remaining: 1m 2s
81:	learn: 0.0574867	total: 5.59s	remaining: 1m 2s
82:	learn: 0.0574783	total: 5.66s	remaining: 1m 2s
83:	learn: 0.0574128	total: 5.74s	remaining: 1m 2s
84:	learn: 0.0572669	total: 5.81s	remaining: 1m 2s
85:	learn: 0.0572419	total: 5.87s	remaining: 1m 2s
86:	learn: 0.0572231	total: 5.93s	remaining: 1m 2s
87:	learn: 0.0571865	total: 5.99s	remaining: 1m 2s
88:	learn: 0.0570117	total: 6.07s	remaining: 1m 2s
89:	learn: 0.0569729	total: 6.12s	remaining: 1m 1s
90:	learn: 0.0568291	total: 6.18s	remaining: 1m 1s
91:	learn: 0.0568085	total: 6.23s	remaining: 1m 1s
92:	learn: 0.0567982	total: 6.3s	remaining: 1m 1s
93:	learn: 0.0567343	total: 6.36s	remaining: 1m 1s
94:	learn: 0.0567230	total: 6.41s	remaining: 1m 1s
95:	learn: 0.0566994	total: 6.46s	remaining: 1m
96:	learn: 0.0566412	total: 6.52s	remaining: 1m
97:	learn: 0.0566346	total: 6.58s	remaining: 1m
98:	learn: 0.0564661	total: 6.67s	remaining: 1m
99:	learn: 0.0564002	total: 6.74s	remaining: 1m
100:	learn: 0.0562969	total: 6.8s	remaining: 1m
101:	learn: 0.0562237	total: 6.86s	remaining: 1m
102:	learn: 0.0561784	total: 6.93s	remaining: 1m
103:	learn: 0.0561583	total: 6.98s	remaining: 1m
104:	learn: 0.0561431	total: 7.04s	remaining: 1m
105:	learn: 0.0560899	total: 7.12s	remaining: 1m
106:	learn: 0.0560397	total: 7.2s	remaining: 1m
107:	learn: 0.0560123	total: 7.25s	remaining: 59.9s
108:	learn: 0.0559927	total: 7.3s	remaining: 59.7s
109:	learn: 0.0559713	total: 7.37s	remaining: 59.7s
110:	learn: 0.0558813	total: 7.44s	remaining: 59.6s
111:	learn: 0.0558222	total: 7.49s	remaining: 59.4s
112:	learn: 0.0557351	total: 7.56s	remaining: 59.3s
113:	learn: 0.0557114	total: 7.61s	remaining: 59.2s
114:	learn: 0.0556592	total: 7.69s	remaining: 59.2s
115:	learn: 0.0556116	total: 7.75s	remaining: 59s
116:	learn: 0.0554982	total: 7.82s	remaining: 59s
117:	learn: 0.0554500	total: 7.87s	remaining: 58.9s
118:	learn: 0.0553952	total: 7.95s	remaining: 58.9s
119:	learn: 0.0553746	total: 8s	remaining: 58.7s
120:	learn: 0.0553050	total: 8.06s	remaining: 58.5s
121:	learn: 0.0552251	total: 8.11s	remaining: 58.4s
122:	learn: 0.0551906	total: 8.17s	remaining: 58.3s
123:	learn: 0.0551428	total: 8.22s	remaining: 58.1s
124:	learn: 0.0550833	total: 8.29s	remaining: 58s
125:	learn: 0.0550654	total: 8.34s	remaining: 57.8s
126:	learn: 0.0550506	total: 8.39s	remaining: 57.7s
127:	learn: 0.0550224	total: 8.46s	remaining: 57.6s
128:	learn: 0.0550048	total: 8.53s	remaining: 57.6s
129:	learn: 0.0549081	total: 8.59s	remaining: 57.5s
130:	learn: 0.0548545	total: 8.67s	remaining: 57.5s
131:	learn: 0.0548146	total: 8.73s	remaining: 57.4s
132:	learn: 0.0547768	total: 8.78s	remaining: 57.3s
133:	learn: 0.0547117	total: 8.85s	remaining: 57.2s
134:	learn: 0.0546784	total: 8.91s	remaining: 57.1s
135:	learn: 0.0546011	total: 8.97s	remaining: 57s
136:	learn: 0.0545595	total: 9.03s	remaining: 56.9s
137:	learn: 0.0544903	total: 9.1s	remaining: 56.8s
138:	learn: 0.0544336	total: 9.15s	remaining: 56.7s
139:	learn: 0.0544164	total: 9.21s	remaining: 56.6s
140:	learn: 0.0543766	total: 9.27s	remaining: 56.5s
141:	learn: 0.0543502	total: 9.32s	remaining: 56.3s
142:	learn: 0.0543259	total: 9.37s	remaining: 56.2s
143:	learn: 0.0542884	total: 9.43s	remaining: 56s
144:	learn: 0.0542205	total: 9.48s	remaining: 55.9s
145:	learn: 0.0541565	total: 9.54s	remaining: 55.8s
146:	learn: 0.0540800	total: 9.61s	remaining: 55.7s
147:	learn: 0.0540365	total: 9.67s	remaining: 55.6s
148:	learn: 0.0539181	total: 9.74s	remaining: 55.6s
149:	learn: 0.0538727	total: 9.79s	remaining: 55.5s
150:	learn: 0.0538308	total: 9.86s	remaining: 55.5s
151:	learn: 0.0538141	total: 9.91s	remaining: 55.3s
152:	learn: 0.0537693	total: 9.96s	remaining: 55.2s
153:	learn: 0.0536628	total: 10s	remaining: 55.2s
154:	learn: 0.0535924	total: 10.1s	remaining: 55.1s
155:	learn: 0.0535803	total: 10.2s	remaining: 55s
156:	learn: 0.0535351	total: 10.2s	remaining: 54.9s
157:	learn: 0.0534749	total: 10.3s	remaining: 54.8s
158:	learn: 0.0534569	total: 10.3s	remaining: 54.7s
159:	learn: 0.0534080	total: 10.4s	remaining: 54.6s
160:	learn: 0.0533712	total: 10.5s	remaining: 54.5s
161:	learn: 0.0533455	total: 10.5s	remaining: 54.4s
162:	learn: 0.0532847	total: 10.6s	remaining: 54.3s
163:	learn: 0.0532512	total: 10.6s	remaining: 54.2s
164:	learn: 0.0532273	total: 10.7s	remaining: 54.1s
165:	learn: 0.0531658	total: 10.8s	remaining: 54s
166:	learn: 0.0530811	total: 10.8s	remaining: 53.9s
167:	learn: 0.0530271	total: 10.9s	remaining: 53.9s
168:	learn: 0.0529973	total: 10.9s	remaining: 53.8s
169:	learn: 0.0529822	total: 11s	remaining: 53.7s
170:	learn: 0.0529534	total: 11s	remaining: 53.6s
171:	learn: 0.0529394	total: 11.1s	remaining: 53.5s
172:	learn: 0.0528909	total: 11.2s	remaining: 53.4s
173:	learn: 0.0528329	total: 11.3s	remaining: 53.4s
174:	learn: 0.0527777	total: 11.3s	remaining: 53.4s
175:	learn: 0.0527239	total: 11.4s	remaining: 53.3s
176:	learn: 0.0526702	total: 11.5s	remaining: 53.3s
177:	learn: 0.0526160	total: 11.5s	remaining: 53.2s
178:	learn: 0.0526008	total: 11.6s	remaining: 53.1s
179:	learn: 0.0525413	total: 11.6s	remaining: 53s
180:	learn: 0.0524675	total: 11.7s	remaining: 52.9s
181:	learn: 0.0524031	total: 11.8s	remaining: 52.9s
182:	learn: 0.0523093	total: 11.8s	remaining: 52.8s
183:	learn: 0.0523023	total: 11.9s	remaining: 52.8s
184:	learn: 0.0522479	total: 12s	remaining: 52.7s
185:	learn: 0.0521978	total: 12s	remaining: 52.6s
186:	learn: 0.0521256	total: 12.1s	remaining: 52.6s
187:	learn: 0.0520564	total: 12.2s	remaining: 52.5s
188:	learn: 0.0520322	total: 12.2s	remaining: 52.4s
189:	learn: 0.0519680	total: 12.3s	remaining: 52.3s
190:	learn: 0.0518855	total: 12.3s	remaining: 52.3s
191:	learn: 0.0518489	total: 12.4s	remaining: 52.2s
192:	learn: 0.0518082	total: 12.5s	remaining: 52.1s
193:	learn: 0.0517728	total: 12.5s	remaining: 52s
194:	learn: 0.0517184	total: 12.6s	remaining: 51.9s
195:	learn: 0.0516702	total: 12.6s	remaining: 51.8s
196:	learn: 0.0516372	total: 12.7s	remaining: 51.7s
197:	learn: 0.0515686	total: 12.8s	remaining: 51.7s
198:	learn: 0.0514873	total: 12.8s	remaining: 51.7s
199:	learn: 0.0514344	total: 12.9s	remaining: 51.6s
200:	learn: 0.0513846	total: 13s	remaining: 51.5s
201:	learn: 0.0513615	total: 13s	remaining: 51.4s
202:	learn: 0.0513381	total: 13.1s	remaining: 51.3s
203:	learn: 0.0512955	total: 13.1s	remaining: 51.3s
204:	learn: 0.0512026	total: 13.2s	remaining: 51.2s
205:	learn: 0.0511556	total: 13.3s	remaining: 51.1s
206:	learn: 0.0511498	total: 13.3s	remaining: 51s
207:	learn: 0.0511161	total: 13.4s	remaining: 50.9s
208:	learn: 0.0511045	total: 13.4s	remaining: 50.8s
209:	learn: 0.0510426	total: 13.5s	remaining: 50.7s
210:	learn: 0.0510189	total: 13.5s	remaining: 50.6s
211:	learn: 0.0509717	total: 13.6s	remaining: 50.5s
212:	learn: 0.0509365	total: 13.7s	remaining: 50.5s
213:	learn: 0.0508881	total: 13.7s	remaining: 50.5s
214:	learn: 0.0508397	total: 13.8s	remaining: 50.4s
215:	learn: 0.0508141	total: 13.9s	remaining: 50.3s
216:	learn: 0.0508018	total: 13.9s	remaining: 50.2s
217:	learn: 0.0507413	total: 14s	remaining: 50.1s
218:	learn: 0.0506895	total: 14s	remaining: 50s
219:	learn: 0.0506559	total: 14.1s	remaining: 50s
220:	learn: 0.0506094	total: 14.2s	remaining: 49.9s
221:	learn: 0.0505175	total: 14.2s	remaining: 49.8s
222:	learn: 0.0504262	total: 14.3s	remaining: 49.7s
223:	learn: 0.0503715	total: 14.3s	remaining: 49.7s
224:	learn: 0.0503127	total: 14.4s	remaining: 49.6s
225:	learn: 0.0502677	total: 14.4s	remaining: 49.5s
226:	learn: 0.0502448	total: 14.5s	remaining: 49.4s
227:	learn: 0.0501664	total: 14.6s	remaining: 49.3s
228:	learn: 0.0501488	total: 14.6s	remaining: 49.2s
229:	learn: 0.0501067	total: 14.7s	remaining: 49.1s
230:	learn: 0.0500562	total: 14.7s	remaining: 49.1s
231:	learn: 0.0500348	total: 14.8s	remaining: 49s
232:	learn: 0.0499756	total: 14.9s	remaining: 48.9s
233:	learn: 0.0499399	total: 14.9s	remaining: 48.8s
234:	learn: 0.0498573	total: 15s	remaining: 48.8s
235:	learn: 0.0498099	total: 15s	remaining: 48.7s
236:	learn: 0.0497748	total: 15.1s	remaining: 48.6s
237:	learn: 0.0497284	total: 15.2s	remaining: 48.5s
238:	learn: 0.0497037	total: 15.2s	remaining: 48.4s
239:	learn: 0.0496311	total: 15.3s	remaining: 48.4s
240:	learn: 0.0495767	total: 15.3s	remaining: 48.3s
241:	learn: 0.0495408	total: 15.4s	remaining: 48.2s
242:	learn: 0.0495026	total: 15.5s	remaining: 48.2s
243:	learn: 0.0494445	total: 15.5s	remaining: 48.1s
244:	learn: 0.0494423	total: 15.6s	remaining: 48s
245:	learn: 0.0493809	total: 15.6s	remaining: 47.9s
246:	learn: 0.0493157	total: 15.7s	remaining: 47.8s
247:	learn: 0.0492954	total: 15.8s	remaining: 47.8s
248:	learn: 0.0492475	total: 15.8s	remaining: 47.8s
249:	learn: 0.0491226	total: 15.9s	remaining: 47.7s
250:	learn: 0.0490657	total: 16s	remaining: 47.6s
251:	learn: 0.0490209	total: 16s	remaining: 47.5s
252:	learn: 0.0489507	total: 16.1s	remaining: 47.4s
253:	learn: 0.0488949	total: 16.1s	remaining: 47.4s
254:	learn: 0.0488616	total: 16.2s	remaining: 47.3s
255:	learn: 0.0488349	total: 16.2s	remaining: 47.2s
256:	learn: 0.0488226	total: 16.3s	remaining: 47.1s
257:	learn: 0.0487606	total: 16.3s	remaining: 47s
258:	learn: 0.0487007	total: 16.4s	remaining: 46.9s
259:	learn: 0.0486512	total: 16.5s	remaining: 46.9s
260:	learn: 0.0485839	total: 16.5s	remaining: 46.8s
261:	learn: 0.0485347	total: 16.6s	remaining: 46.7s
262:	learn: 0.0485125	total: 16.6s	remaining: 46.6s
263:	learn: 0.0484725	total: 16.7s	remaining: 46.5s
264:	learn: 0.0483935	total: 16.7s	remaining: 46.4s
265:	learn: 0.0483350	total: 16.8s	remaining: 46.4s
266:	learn: 0.0482639	total: 16.9s	remaining: 46.4s
267:	learn: 0.0482333	total: 16.9s	remaining: 46.3s
268:	learn: 0.0481846	total: 17s	remaining: 46.2s
269:	learn: 0.0481628	total: 17.1s	remaining: 46.1s
270:	learn: 0.0480927	total: 17.1s	remaining: 46.1s
271:	learn: 0.0480491	total: 17.2s	remaining: 46s
272:	learn: 0.0480007	total: 17.3s	remaining: 45.9s
273:	learn: 0.0479629	total: 17.3s	remaining: 45.9s
274:	learn: 0.0478177	total: 17.4s	remaining: 45.8s
275:	learn: 0.0477302	total: 17.4s	remaining: 45.8s
276:	learn: 0.0477281	total: 17.5s	remaining: 45.6s
277:	learn: 0.0476667	total: 17.6s	remaining: 45.6s
278:	learn: 0.0476606	total: 17.6s	remaining: 45.5s
279:	learn: 0.0476008	total: 17.7s	remaining: 45.4s
280:	learn: 0.0475490	total: 17.7s	remaining: 45.4s
281:	learn: 0.0475189	total: 17.8s	remaining: 45.3s
282:	learn: 0.0474867	total: 17.9s	remaining: 45.2s
283:	learn: 0.0474538	total: 17.9s	remaining: 45.2s
284:	learn: 0.0474022	total: 18s	remaining: 45.1s
285:	learn: 0.0473273	total: 18s	remaining: 45s
286:	learn: 0.0472791	total: 18.1s	remaining: 45s
287:	learn: 0.0472691	total: 18.2s	remaining: 44.9s
288:	learn: 0.0472671	total: 18.2s	remaining: 44.8s
289:	learn: 0.0471974	total: 18.3s	remaining: 44.7s
290:	learn: 0.0471047	total: 18.4s	remaining: 44.7s
291:	learn: 0.0470549	total: 18.4s	remaining: 44.6s
292:	learn: 0.0470379	total: 18.5s	remaining: 44.5s
293:	learn: 0.0469760	total: 18.5s	remaining: 44.5s
294:	learn: 0.0469089	total: 18.6s	remaining: 44.4s
295:	learn: 0.0468923	total: 18.6s	remaining: 44.3s
296:	learn: 0.0468408	total: 18.7s	remaining: 44.2s
297:	learn: 0.0468056	total: 18.7s	remaining: 44.1s
298:	learn: 0.0467504	total: 18.8s	remaining: 44.1s
299:	learn: 0.0467299	total: 18.9s	remaining: 44s
300:	learn: 0.0466675	total: 18.9s	remaining: 43.9s
301:	learn: 0.0466257	total: 19s	remaining: 43.9s
302:	learn: 0.0465548	total: 19s	remaining: 43.8s
303:	learn: 0.0465268	total: 19.1s	remaining: 43.7s
304:	learn: 0.0465252	total: 19.2s	remaining: 43.6s
305:	learn: 0.0464259	total: 19.2s	remaining: 43.6s
306:	learn: 0.0463530	total: 19.3s	remaining: 43.6s
307:	learn: 0.0463249	total: 19.4s	remaining: 43.5s
308:	learn: 0.0462750	total: 19.4s	remaining: 43.4s
309:	learn: 0.0462630	total: 19.5s	remaining: 43.4s
310:	learn: 0.0462297	total: 19.5s	remaining: 43.3s
311:	learn: 0.0461664	total: 19.6s	remaining: 43.2s
312:	learn: 0.0461085	total: 19.7s	remaining: 43.1s
313:	learn: 0.0460780	total: 19.7s	remaining: 43.1s
314:	learn: 0.0460085	total: 19.8s	remaining: 43s
315:	learn: 0.0459182	total: 19.8s	remaining: 42.9s
316:	learn: 0.0458226	total: 19.9s	remaining: 42.9s
317:	learn: 0.0457874	total: 20s	remaining: 42.8s
318:	learn: 0.0457476	total: 20s	remaining: 42.7s
319:	learn: 0.0457063	total: 20.1s	remaining: 42.7s
320:	learn: 0.0456787	total: 20.1s	remaining: 42.6s
321:	learn: 0.0456257	total: 20.2s	remaining: 42.5s
322:	learn: 0.0455600	total: 20.3s	remaining: 42.5s
323:	learn: 0.0455555	total: 20.3s	remaining: 42.4s
324:	learn: 0.0454599	total: 20.4s	remaining: 42.3s
325:	learn: 0.0453942	total: 20.4s	remaining: 42.3s
326:	learn: 0.0453398	total: 20.5s	remaining: 42.2s
327:	learn: 0.0452512	total: 20.6s	remaining: 42.2s
328:	learn: 0.0452497	total: 20.6s	remaining: 42.1s
329:	learn: 0.0452482	total: 20.7s	remaining: 42s
330:	learn: 0.0451938	total: 20.7s	remaining: 41.9s
331:	learn: 0.0451691	total: 20.8s	remaining: 41.8s
332:	learn: 0.0451307	total: 20.8s	remaining: 41.8s
333:	learn: 0.0450548	total: 20.9s	remaining: 41.7s
334:	learn: 0.0449920	total: 21s	remaining: 41.6s
335:	learn: 0.0449669	total: 21s	remaining: 41.6s
336:	learn: 0.0448999	total: 21.1s	remaining: 41.5s
337:	learn: 0.0448705	total: 21.2s	remaining: 41.4s
338:	learn: 0.0448570	total: 21.2s	remaining: 41.3s
339:	learn: 0.0448304	total: 21.3s	remaining: 41.3s
340:	learn: 0.0447365	total: 21.3s	remaining: 41.2s
341:	learn: 0.0446825	total: 21.4s	remaining: 41.2s
342:	learn: 0.0446405	total: 21.4s	remaining: 41.1s
343:	learn: 0.0446134	total: 21.5s	remaining: 41s
344:	learn: 0.0445275	total: 21.6s	remaining: 40.9s
345:	learn: 0.0445124	total: 21.6s	remaining: 40.8s
346:	learn: 0.0444338	total: 21.7s	remaining: 40.8s
347:	learn: 0.0443815	total: 21.7s	remaining: 40.7s
348:	learn: 0.0443692	total: 21.8s	remaining: 40.6s
349:	learn: 0.0443047	total: 21.8s	remaining: 40.5s
350:	learn: 0.0442479	total: 21.9s	remaining: 40.5s
351:	learn: 0.0442129	total: 21.9s	remaining: 40.4s
352:	learn: 0.0441724	total: 22s	remaining: 40.3s
353:	learn: 0.0441173	total: 22.1s	remaining: 40.3s
354:	learn: 0.0440717	total: 22.1s	remaining: 40.2s
355:	learn: 0.0440586	total: 22.2s	remaining: 40.1s
356:	learn: 0.0440028	total: 22.2s	remaining: 40s
357:	learn: 0.0439518	total: 22.3s	remaining: 40s
358:	learn: 0.0438558	total: 22.4s	remaining: 39.9s
359:	learn: 0.0438170	total: 22.4s	remaining: 39.9s
360:	learn: 0.0438063	total: 22.5s	remaining: 39.8s
361:	learn: 0.0437353	total: 22.5s	remaining: 39.7s
362:	learn: 0.0437072	total: 22.6s	remaining: 39.7s
363:	learn: 0.0436388	total: 22.7s	remaining: 39.6s
364:	learn: 0.0435817	total: 22.7s	remaining: 39.5s
365:	learn: 0.0435485	total: 22.8s	remaining: 39.5s
366:	learn: 0.0434686	total: 22.9s	remaining: 39.4s
367:	learn: 0.0434145	total: 22.9s	remaining: 39.4s
368:	learn: 0.0434066	total: 23s	remaining: 39.3s
369:	learn: 0.0433175	total: 23s	remaining: 39.2s
370:	learn: 0.0432771	total: 23.1s	remaining: 39.2s
371:	learn: 0.0432258	total: 23.2s	remaining: 39.1s
372:	learn: 0.0432180	total: 23.2s	remaining: 39s
373:	learn: 0.0431786	total: 23.3s	remaining: 39s
374:	learn: 0.0431298	total: 23.3s	remaining: 38.9s
375:	learn: 0.0430893	total: 23.4s	remaining: 38.8s
376:	learn: 0.0430383	total: 23.4s	remaining: 38.7s
377:	learn: 0.0429843	total: 23.5s	remaining: 38.7s
378:	learn: 0.0429334	total: 23.6s	remaining: 38.6s
379:	learn: 0.0429061	total: 23.6s	remaining: 38.5s
380:	learn: 0.0428599	total: 23.7s	remaining: 38.4s
381:	learn: 0.0428477	total: 23.7s	remaining: 38.4s
382:	learn: 0.0428339	total: 23.8s	remaining: 38.3s
383:	learn: 0.0427626	total: 23.8s	remaining: 38.2s
384:	learn: 0.0426839	total: 23.9s	remaining: 38.2s
385:	learn: 0.0426132	total: 24s	remaining: 38.1s
386:	learn: 0.0425797	total: 24s	remaining: 38.1s
387:	learn: 0.0425194	total: 24.1s	remaining: 38s
388:	learn: 0.0424997	total: 24.1s	remaining: 37.9s
389:	learn: 0.0424470	total: 24.2s	remaining: 37.8s
390:	learn: 0.0424176	total: 24.2s	remaining: 37.8s
391:	learn: 0.0423932	total: 24.3s	remaining: 37.7s
392:	learn: 0.0423400	total: 24.4s	remaining: 37.6s
393:	learn: 0.0422555	total: 24.4s	remaining: 37.6s
394:	learn: 0.0422084	total: 24.5s	remaining: 37.5s
395:	learn: 0.0421961	total: 24.5s	remaining: 37.4s
396:	learn: 0.0421829	total: 24.6s	remaining: 37.4s
397:	learn: 0.0421459	total: 24.6s	remaining: 37.3s
398:	learn: 0.0420793	total: 24.7s	remaining: 37.2s
399:	learn: 0.0420737	total: 24.8s	remaining: 37.1s
400:	learn: 0.0420055	total: 24.8s	remaining: 37.1s
401:	learn: 0.0419568	total: 24.9s	remaining: 37s
402:	learn: 0.0419423	total: 24.9s	remaining: 36.9s
403:	learn: 0.0418847	total: 25s	remaining: 36.9s
404:	learn: 0.0418641	total: 25.1s	remaining: 36.8s
405:	learn: 0.0417861	total: 25.1s	remaining: 36.7s
406:	learn: 0.0417461	total: 25.2s	remaining: 36.7s
407:	learn: 0.0416744	total: 25.2s	remaining: 36.6s
408:	learn: 0.0416470	total: 25.3s	remaining: 36.5s
409:	learn: 0.0416281	total: 25.3s	remaining: 36.5s
410:	learn: 0.0416134	total: 25.4s	remaining: 36.4s
411:	learn: 0.0415977	total: 25.4s	remaining: 36.3s
412:	learn: 0.0415144	total: 25.5s	remaining: 36.3s
413:	learn: 0.0415064	total: 25.6s	remaining: 36.2s
414:	learn: 0.0414533	total: 25.6s	remaining: 36.1s
415:	learn: 0.0414225	total: 25.7s	remaining: 36s
416:	learn: 0.0413688	total: 25.7s	remaining: 36s
417:	learn: 0.0413232	total: 25.8s	remaining: 35.9s
418:	learn: 0.0412796	total: 25.8s	remaining: 35.8s
419:	learn: 0.0412368	total: 25.9s	remaining: 35.7s
420:	learn: 0.0411821	total: 25.9s	remaining: 35.7s
421:	learn: 0.0411473	total: 26s	remaining: 35.6s
422:	learn: 0.0410605	total: 26.1s	remaining: 35.6s
423:	learn: 0.0410421	total: 26.1s	remaining: 35.5s
424:	learn: 0.0410225	total: 26.2s	remaining: 35.4s
425:	learn: 0.0409891	total: 26.2s	remaining: 35.4s
426:	learn: 0.0409161	total: 26.3s	remaining: 35.3s
427:	learn: 0.0408767	total: 26.4s	remaining: 35.2s
428:	learn: 0.0408403	total: 26.4s	remaining: 35.2s
429:	learn: 0.0408149	total: 26.5s	remaining: 35.1s
430:	learn: 0.0407938	total: 26.5s	remaining: 35s
431:	learn: 0.0407623	total: 26.6s	remaining: 35s
432:	learn: 0.0407228	total: 26.7s	remaining: 34.9s
433:	learn: 0.0406866	total: 26.7s	remaining: 34.8s
434:	learn: 0.0406557	total: 26.8s	remaining: 34.8s
435:	learn: 0.0406054	total: 26.8s	remaining: 34.7s
436:	learn: 0.0405192	total: 26.9s	remaining: 34.7s
437:	learn: 0.0404833	total: 27s	remaining: 34.6s
438:	learn: 0.0404263	total: 27s	remaining: 34.5s
439:	learn: 0.0403643	total: 27.1s	remaining: 34.5s
440:	learn: 0.0403156	total: 27.2s	remaining: 34.4s
441:	learn: 0.0402993	total: 27.2s	remaining: 34.3s
442:	learn: 0.0402683	total: 27.3s	remaining: 34.3s
443:	learn: 0.0401922	total: 27.3s	remaining: 34.2s
444:	learn: 0.0401374	total: 27.4s	remaining: 34.2s
445:	learn: 0.0401277	total: 27.4s	remaining: 34.1s
446:	learn: 0.0400976	total: 27.5s	remaining: 34s
447:	learn: 0.0400631	total: 27.6s	remaining: 34s
448:	learn: 0.0399950	total: 27.6s	remaining: 33.9s
449:	learn: 0.0399237	total: 27.7s	remaining: 33.8s
450:	learn: 0.0398581	total: 27.7s	remaining: 33.8s
451:	learn: 0.0398450	total: 27.8s	remaining: 33.7s
452:	learn: 0.0398280	total: 27.9s	remaining: 33.6s
453:	learn: 0.0397748	total: 27.9s	remaining: 33.6s
454:	learn: 0.0397406	total: 28s	remaining: 33.5s
455:	learn: 0.0396733	total: 28.1s	remaining: 33.5s
456:	learn: 0.0396486	total: 28.1s	remaining: 33.4s
457:	learn: 0.0395987	total: 28.2s	remaining: 33.3s
458:	learn: 0.0395330	total: 28.2s	remaining: 33.3s
459:	learn: 0.0395130	total: 28.3s	remaining: 33.2s
460:	learn: 0.0394525	total: 28.4s	remaining: 33.2s
461:	learn: 0.0394439	total: 28.4s	remaining: 33.1s
462:	learn: 0.0394072	total: 28.5s	remaining: 33s
463:	learn: 0.0393594	total: 28.5s	remaining: 33s
464:	learn: 0.0393286	total: 28.6s	remaining: 32.9s
465:	learn: 0.0392917	total: 28.7s	remaining: 32.8s
466:	learn: 0.0392615	total: 28.7s	remaining: 32.8s
467:	learn: 0.0392371	total: 28.8s	remaining: 32.7s
468:	learn: 0.0392098	total: 28.9s	remaining: 32.7s
469:	learn: 0.0391392	total: 28.9s	remaining: 32.6s
470:	learn: 0.0391276	total: 29s	remaining: 32.5s
471:	learn: 0.0390537	total: 29s	remaining: 32.5s
472:	learn: 0.0389703	total: 29.1s	remaining: 32.4s
473:	learn: 0.0388994	total: 29.2s	remaining: 32.4s
474:	learn: 0.0388547	total: 29.2s	remaining: 32.3s
475:	learn: 0.0388125	total: 29.3s	remaining: 32.3s
476:	learn: 0.0387571	total: 29.4s	remaining: 32.2s
477:	learn: 0.0387449	total: 29.4s	remaining: 32.1s
478:	learn: 0.0387140	total: 29.5s	remaining: 32.1s
479:	learn: 0.0386776	total: 29.5s	remaining: 32s
480:	learn: 0.0386018	total: 29.6s	remaining: 31.9s
481:	learn: 0.0385483	total: 29.7s	remaining: 31.9s
482:	learn: 0.0385026	total: 29.7s	remaining: 31.8s
483:	learn: 0.0384540	total: 29.8s	remaining: 31.7s
484:	learn: 0.0383958	total: 29.8s	remaining: 31.7s
485:	learn: 0.0383401	total: 29.9s	remaining: 31.6s
486:	learn: 0.0383130	total: 30s	remaining: 31.6s
487:	learn: 0.0382772	total: 30s	remaining: 31.5s
488:	learn: 0.0382616	total: 30.1s	remaining: 31.4s
489:	learn: 0.0381778	total: 30.1s	remaining: 31.4s
490:	learn: 0.0381321	total: 30.2s	remaining: 31.3s
491:	learn: 0.0380992	total: 30.3s	remaining: 31.3s
492:	learn: 0.0380603	total: 30.3s	remaining: 31.2s
493:	learn: 0.0380471	total: 30.4s	remaining: 31.1s
494:	learn: 0.0379969	total: 30.4s	remaining: 31.1s
495:	learn: 0.0379557	total: 30.5s	remaining: 31s
496:	learn: 0.0379311	total: 30.6s	remaining: 30.9s
497:	learn: 0.0378943	total: 30.6s	remaining: 30.9s
498:	learn: 0.0378843	total: 30.7s	remaining: 30.8s
499:	learn: 0.0378126	total: 30.7s	remaining: 30.7s
500:	learn: 0.0377782	total: 30.8s	remaining: 30.7s
501:	learn: 0.0377637	total: 30.8s	remaining: 30.6s
502:	learn: 0.0377544	total: 30.9s	remaining: 30.5s
503:	learn: 0.0377063	total: 31s	remaining: 30.5s
504:	learn: 0.0376640	total: 31s	remaining: 30.4s
505:	learn: 0.0376418	total: 31.1s	remaining: 30.3s
506:	learn: 0.0375838	total: 31.1s	remaining: 30.3s
507:	learn: 0.0375472	total: 31.2s	remaining: 30.2s
508:	learn: 0.0374868	total: 31.3s	remaining: 30.2s
509:	learn: 0.0374442	total: 31.4s	remaining: 30.1s
510:	learn: 0.0374159	total: 31.4s	remaining: 30.1s
511:	learn: 0.0373850	total: 31.5s	remaining: 30s
512:	learn: 0.0373291	total: 31.5s	remaining: 29.9s
513:	learn: 0.0372919	total: 31.6s	remaining: 29.9s
514:	learn: 0.0372737	total: 31.6s	remaining: 29.8s
515:	learn: 0.0372226	total: 31.7s	remaining: 29.7s
516:	learn: 0.0372150	total: 31.7s	remaining: 29.7s
517:	learn: 0.0372057	total: 31.8s	remaining: 29.6s
518:	learn: 0.0371850	total: 31.8s	remaining: 29.5s
519:	learn: 0.0371457	total: 31.9s	remaining: 29.5s
520:	learn: 0.0370935	total: 32s	remaining: 29.4s
521:	learn: 0.0370883	total: 32s	remaining: 29.3s
522:	learn: 0.0370598	total: 32.1s	remaining: 29.3s
523:	learn: 0.0370145	total: 32.2s	remaining: 29.2s
524:	learn: 0.0369421	total: 32.2s	remaining: 29.2s
525:	learn: 0.0368971	total: 32.3s	remaining: 29.1s
526:	learn: 0.0368783	total: 32.3s	remaining: 29s
527:	learn: 0.0368711	total: 32.4s	remaining: 29s
528:	learn: 0.0368299	total: 32.5s	remaining: 28.9s
529:	learn: 0.0367851	total: 32.5s	remaining: 28.8s
530:	learn: 0.0367192	total: 32.6s	remaining: 28.8s
531:	learn: 0.0366994	total: 32.6s	remaining: 28.7s
532:	learn: 0.0366703	total: 32.7s	remaining: 28.7s
533:	learn: 0.0366323	total: 32.8s	remaining: 28.6s
534:	learn: 0.0365964	total: 32.8s	remaining: 28.5s
535:	learn: 0.0365350	total: 32.9s	remaining: 28.5s
536:	learn: 0.0365081	total: 32.9s	remaining: 28.4s
537:	learn: 0.0364988	total: 33s	remaining: 28.3s
538:	learn: 0.0364262	total: 33.1s	remaining: 28.3s
539:	learn: 0.0363926	total: 33.1s	remaining: 28.2s
540:	learn: 0.0363820	total: 33.2s	remaining: 28.2s
541:	learn: 0.0363237	total: 33.2s	remaining: 28.1s
542:	learn: 0.0362975	total: 33.3s	remaining: 28s
543:	learn: 0.0362732	total: 33.4s	remaining: 28s
544:	learn: 0.0362353	total: 33.4s	remaining: 27.9s
545:	learn: 0.0361857	total: 33.5s	remaining: 27.8s
546:	learn: 0.0361213	total: 33.6s	remaining: 27.8s
547:	learn: 0.0360844	total: 33.6s	remaining: 27.7s
548:	learn: 0.0360674	total: 33.7s	remaining: 27.7s
549:	learn: 0.0360470	total: 33.7s	remaining: 27.6s
550:	learn: 0.0360176	total: 33.8s	remaining: 27.5s
551:	learn: 0.0359420	total: 33.9s	remaining: 27.5s
552:	learn: 0.0359138	total: 33.9s	remaining: 27.4s
553:	learn: 0.0358979	total: 34s	remaining: 27.3s
554:	learn: 0.0358708	total: 34s	remaining: 27.3s
555:	learn: 0.0358425	total: 34.1s	remaining: 27.2s
556:	learn: 0.0358036	total: 34.1s	remaining: 27.2s
557:	learn: 0.0357916	total: 34.2s	remaining: 27.1s
558:	learn: 0.0357564	total: 34.3s	remaining: 27s
559:	learn: 0.0356806	total: 34.3s	remaining: 27s
560:	learn: 0.0355960	total: 34.4s	remaining: 26.9s
561:	learn: 0.0355674	total: 34.5s	remaining: 26.9s
562:	learn: 0.0355322	total: 34.5s	remaining: 26.8s
563:	learn: 0.0354729	total: 34.6s	remaining: 26.7s
564:	learn: 0.0354377	total: 34.6s	remaining: 26.7s
565:	learn: 0.0354195	total: 34.7s	remaining: 26.6s
566:	learn: 0.0353908	total: 34.7s	remaining: 26.5s
567:	learn: 0.0352936	total: 34.8s	remaining: 26.5s
568:	learn: 0.0352565	total: 34.9s	remaining: 26.4s
569:	learn: 0.0352331	total: 34.9s	remaining: 26.3s
570:	learn: 0.0351967	total: 35s	remaining: 26.3s
571:	learn: 0.0351712	total: 35s	remaining: 26.2s
572:	learn: 0.0351136	total: 35.1s	remaining: 26.2s
573:	learn: 0.0350591	total: 35.2s	remaining: 26.1s
574:	learn: 0.0350075	total: 35.2s	remaining: 26s
575:	learn: 0.0349765	total: 35.3s	remaining: 26s
576:	learn: 0.0349523	total: 35.4s	remaining: 25.9s
577:	learn: 0.0349277	total: 35.4s	remaining: 25.9s
578:	learn: 0.0348658	total: 35.5s	remaining: 25.8s
579:	learn: 0.0348529	total: 35.5s	remaining: 25.7s
580:	learn: 0.0347956	total: 35.6s	remaining: 25.7s
581:	learn: 0.0347615	total: 35.7s	remaining: 25.6s
582:	learn: 0.0347416	total: 35.7s	remaining: 25.5s
583:	learn: 0.0346765	total: 35.8s	remaining: 25.5s
584:	learn: 0.0346665	total: 35.8s	remaining: 25.4s
585:	learn: 0.0346513	total: 35.9s	remaining: 25.4s
586:	learn: 0.0346356	total: 35.9s	remaining: 25.3s
587:	learn: 0.0346086	total: 36s	remaining: 25.2s
588:	learn: 0.0345367	total: 36.1s	remaining: 25.2s
589:	learn: 0.0344823	total: 36.1s	remaining: 25.1s
590:	learn: 0.0344377	total: 36.2s	remaining: 25.1s
591:	learn: 0.0344213	total: 36.3s	remaining: 25s
592:	learn: 0.0343740	total: 36.3s	remaining: 24.9s
593:	learn: 0.0343628	total: 36.4s	remaining: 24.9s
594:	learn: 0.0343126	total: 36.5s	remaining: 24.8s
595:	learn: 0.0342636	total: 36.5s	remaining: 24.8s
596:	learn: 0.0342408	total: 36.6s	remaining: 24.7s
597:	learn: 0.0342141	total: 36.6s	remaining: 24.6s
598:	learn: 0.0341905	total: 36.7s	remaining: 24.6s
599:	learn: 0.0341558	total: 36.7s	remaining: 24.5s
600:	learn: 0.0341181	total: 36.8s	remaining: 24.4s
601:	learn: 0.0340883	total: 36.9s	remaining: 24.4s
602:	learn: 0.0340714	total: 36.9s	remaining: 24.3s
603:	learn: 0.0339999	total: 37s	remaining: 24.3s
604:	learn: 0.0339759	total: 37s	remaining: 24.2s
605:	learn: 0.0339459	total: 37.1s	remaining: 24.1s
606:	learn: 0.0338894	total: 37.2s	remaining: 24.1s
607:	learn: 0.0338449	total: 37.2s	remaining: 24s
608:	learn: 0.0338207	total: 37.3s	remaining: 23.9s
609:	learn: 0.0337865	total: 37.4s	remaining: 23.9s
610:	learn: 0.0337405	total: 37.4s	remaining: 23.8s
611:	learn: 0.0336958	total: 37.5s	remaining: 23.8s
612:	learn: 0.0336738	total: 37.5s	remaining: 23.7s
613:	learn: 0.0336416	total: 37.6s	remaining: 23.6s
614:	learn: 0.0335961	total: 37.7s	remaining: 23.6s
615:	learn: 0.0335643	total: 37.7s	remaining: 23.5s
616:	learn: 0.0335581	total: 37.8s	remaining: 23.5s
617:	learn: 0.0335446	total: 37.9s	remaining: 23.4s
618:	learn: 0.0335115	total: 37.9s	remaining: 23.3s
619:	learn: 0.0334570	total: 38s	remaining: 23.3s
620:	learn: 0.0333841	total: 38s	remaining: 23.2s
621:	learn: 0.0333431	total: 38.1s	remaining: 23.1s
622:	learn: 0.0333046	total: 38.2s	remaining: 23.1s
623:	learn: 0.0332649	total: 38.2s	remaining: 23s
624:	learn: 0.0332220	total: 38.3s	remaining: 23s
625:	learn: 0.0331918	total: 38.4s	remaining: 22.9s
626:	learn: 0.0331641	total: 38.4s	remaining: 22.9s
627:	learn: 0.0331337	total: 38.5s	remaining: 22.8s
628:	learn: 0.0330990	total: 38.5s	remaining: 22.7s
629:	learn: 0.0330817	total: 38.6s	remaining: 22.7s
630:	learn: 0.0330645	total: 38.6s	remaining: 22.6s
631:	learn: 0.0330028	total: 38.7s	remaining: 22.5s
632:	learn: 0.0329828	total: 38.8s	remaining: 22.5s
633:	learn: 0.0329465	total: 38.8s	remaining: 22.4s
634:	learn: 0.0329374	total: 38.9s	remaining: 22.3s
635:	learn: 0.0328822	total: 38.9s	remaining: 22.3s
636:	learn: 0.0328693	total: 39s	remaining: 22.2s
637:	learn: 0.0328625	total: 39s	remaining: 22.2s
638:	learn: 0.0328496	total: 39.1s	remaining: 22.1s
639:	learn: 0.0328259	total: 39.2s	remaining: 22s
640:	learn: 0.0327972	total: 39.2s	remaining: 22s
641:	learn: 0.0327790	total: 39.3s	remaining: 21.9s
642:	learn: 0.0327586	total: 39.3s	remaining: 21.8s
643:	learn: 0.0327335	total: 39.4s	remaining: 21.8s
644:	learn: 0.0326785	total: 39.4s	remaining: 21.7s
645:	learn: 0.0326307	total: 39.5s	remaining: 21.7s
646:	learn: 0.0326037	total: 39.6s	remaining: 21.6s
647:	learn: 0.0325766	total: 39.7s	remaining: 21.5s
648:	learn: 0.0325232	total: 39.7s	remaining: 21.5s
649:	learn: 0.0324714	total: 39.8s	remaining: 21.4s
650:	learn: 0.0324561	total: 39.9s	remaining: 21.4s
651:	learn: 0.0323986	total: 39.9s	remaining: 21.3s
652:	learn: 0.0323669	total: 40s	remaining: 21.2s
653:	learn: 0.0323593	total: 40s	remaining: 21.2s
654:	learn: 0.0323385	total: 40.1s	remaining: 21.1s
655:	learn: 0.0323346	total: 40.1s	remaining: 21s
656:	learn: 0.0323119	total: 40.2s	remaining: 21s
657:	learn: 0.0322768	total: 40.3s	remaining: 20.9s
658:	learn: 0.0322623	total: 40.3s	remaining: 20.9s
659:	learn: 0.0322489	total: 40.4s	remaining: 20.8s
660:	learn: 0.0322031	total: 40.4s	remaining: 20.7s
661:	learn: 0.0321926	total: 40.5s	remaining: 20.7s
662:	learn: 0.0321804	total: 40.5s	remaining: 20.6s
663:	learn: 0.0321541	total: 40.6s	remaining: 20.5s
664:	learn: 0.0321109	total: 40.7s	remaining: 20.5s
665:	learn: 0.0321024	total: 40.7s	remaining: 20.4s
666:	learn: 0.0320781	total: 40.8s	remaining: 20.4s
667:	learn: 0.0320300	total: 40.8s	remaining: 20.3s
668:	learn: 0.0319844	total: 40.9s	remaining: 20.2s
669:	learn: 0.0319663	total: 41s	remaining: 20.2s
670:	learn: 0.0319361	total: 41s	remaining: 20.1s
671:	learn: 0.0319087	total: 41.1s	remaining: 20.1s
672:	learn: 0.0318745	total: 41.2s	remaining: 20s
673:	learn: 0.0318598	total: 41.2s	remaining: 19.9s
674:	learn: 0.0318538	total: 41.3s	remaining: 19.9s
675:	learn: 0.0318182	total: 41.3s	remaining: 19.8s
676:	learn: 0.0318001	total: 41.4s	remaining: 19.7s
677:	learn: 0.0317899	total: 41.4s	remaining: 19.7s
678:	learn: 0.0317730	total: 41.5s	remaining: 19.6s
679:	learn: 0.0317630	total: 41.5s	remaining: 19.5s
680:	learn: 0.0317351	total: 41.6s	remaining: 19.5s
681:	learn: 0.0317259	total: 41.7s	remaining: 19.4s
682:	learn: 0.0316864	total: 41.7s	remaining: 19.4s
683:	learn: 0.0316500	total: 41.8s	remaining: 19.3s
684:	learn: 0.0316286	total: 41.8s	remaining: 19.2s
685:	learn: 0.0316241	total: 41.9s	remaining: 19.2s
686:	learn: 0.0316003	total: 42s	remaining: 19.1s
687:	learn: 0.0315809	total: 42s	remaining: 19s
688:	learn: 0.0315251	total: 42.1s	remaining: 19s
689:	learn: 0.0315161	total: 42.1s	remaining: 18.9s
690:	learn: 0.0314746	total: 42.2s	remaining: 18.9s
691:	learn: 0.0314413	total: 42.2s	remaining: 18.8s
692:	learn: 0.0314170	total: 42.3s	remaining: 18.8s
693:	learn: 0.0313853	total: 42.4s	remaining: 18.7s
694:	learn: 0.0313630	total: 42.5s	remaining: 18.6s
695:	learn: 0.0313512	total: 42.5s	remaining: 18.6s
696:	learn: 0.0313408	total: 42.6s	remaining: 18.5s
697:	learn: 0.0313290	total: 42.6s	remaining: 18.4s
698:	learn: 0.0313079	total: 42.7s	remaining: 18.4s
699:	learn: 0.0312733	total: 42.7s	remaining: 18.3s
700:	learn: 0.0312551	total: 42.8s	remaining: 18.3s
701:	learn: 0.0312210	total: 42.8s	remaining: 18.2s
702:	learn: 0.0312059	total: 42.9s	remaining: 18.1s
703:	learn: 0.0311576	total: 43s	remaining: 18.1s
704:	learn: 0.0311523	total: 43s	remaining: 18s
705:	learn: 0.0311432	total: 43.1s	remaining: 17.9s
706:	learn: 0.0310774	total: 43.1s	remaining: 17.9s
707:	learn: 0.0310342	total: 43.2s	remaining: 17.8s
708:	learn: 0.0310242	total: 43.2s	remaining: 17.8s
709:	learn: 0.0309840	total: 43.3s	remaining: 17.7s
710:	learn: 0.0309495	total: 43.4s	remaining: 17.6s
711:	learn: 0.0309137	total: 43.5s	remaining: 17.6s
712:	learn: 0.0308928	total: 43.5s	remaining: 17.5s
713:	learn: 0.0308686	total: 43.6s	remaining: 17.4s
714:	learn: 0.0308406	total: 43.6s	remaining: 17.4s
715:	learn: 0.0308158	total: 43.7s	remaining: 17.3s
716:	learn: 0.0307700	total: 43.8s	remaining: 17.3s
717:	learn: 0.0307242	total: 43.8s	remaining: 17.2s
718:	learn: 0.0306894	total: 43.9s	remaining: 17.1s
719:	learn: 0.0306862	total: 43.9s	remaining: 17.1s
720:	learn: 0.0306773	total: 44s	remaining: 17s
721:	learn: 0.0306657	total: 44s	remaining: 17s
722:	learn: 0.0306312	total: 44.1s	remaining: 16.9s
723:	learn: 0.0306221	total: 44.1s	remaining: 16.8s
724:	learn: 0.0306098	total: 44.2s	remaining: 16.8s
725:	learn: 0.0305704	total: 44.3s	remaining: 16.7s
726:	learn: 0.0305514	total: 44.3s	remaining: 16.6s
727:	learn: 0.0305167	total: 44.4s	remaining: 16.6s
728:	learn: 0.0305107	total: 44.4s	remaining: 16.5s
729:	learn: 0.0305026	total: 44.5s	remaining: 16.5s
730:	learn: 0.0304657	total: 44.6s	remaining: 16.4s
731:	learn: 0.0304423	total: 44.6s	remaining: 16.3s
732:	learn: 0.0304039	total: 44.7s	remaining: 16.3s
733:	learn: 0.0303564	total: 44.8s	remaining: 16.2s
734:	learn: 0.0303442	total: 44.8s	remaining: 16.2s
735:	learn: 0.0303344	total: 44.9s	remaining: 16.1s
736:	learn: 0.0303104	total: 44.9s	remaining: 16s
737:	learn: 0.0302935	total: 45s	remaining: 16s
738:	learn: 0.0302394	total: 45s	remaining: 15.9s
739:	learn: 0.0302120	total: 45.1s	remaining: 15.8s
740:	learn: 0.0302052	total: 45.1s	remaining: 15.8s
741:	learn: 0.0301857	total: 45.2s	remaining: 15.7s
742:	learn: 0.0301368	total: 45.3s	remaining: 15.7s
743:	learn: 0.0301146	total: 45.3s	remaining: 15.6s
744:	learn: 0.0300907	total: 45.4s	remaining: 15.5s
745:	learn: 0.0300679	total: 45.5s	remaining: 15.5s
746:	learn: 0.0299999	total: 45.5s	remaining: 15.4s
747:	learn: 0.0299803	total: 45.6s	remaining: 15.4s
748:	learn: 0.0299102	total: 45.6s	remaining: 15.3s
749:	learn: 0.0298624	total: 45.7s	remaining: 15.2s
750:	learn: 0.0298350	total: 45.8s	remaining: 15.2s
751:	learn: 0.0297795	total: 45.8s	remaining: 15.1s
752:	learn: 0.0297468	total: 45.9s	remaining: 15.1s
753:	learn: 0.0297377	total: 45.9s	remaining: 15s
754:	learn: 0.0297231	total: 46s	remaining: 14.9s
755:	learn: 0.0296921	total: 46.1s	remaining: 14.9s
756:	learn: 0.0296599	total: 46.1s	remaining: 14.8s
757:	learn: 0.0296386	total: 46.2s	remaining: 14.7s
758:	learn: 0.0296331	total: 46.3s	remaining: 14.7s
759:	learn: 0.0295906	total: 46.3s	remaining: 14.6s
760:	learn: 0.0295716	total: 46.4s	remaining: 14.6s
761:	learn: 0.0295533	total: 46.4s	remaining: 14.5s
762:	learn: 0.0295113	total: 46.5s	remaining: 14.4s
763:	learn: 0.0294721	total: 46.6s	remaining: 14.4s
764:	learn: 0.0294564	total: 46.6s	remaining: 14.3s
765:	learn: 0.0294153	total: 46.7s	remaining: 14.3s
766:	learn: 0.0293932	total: 46.8s	remaining: 14.2s
767:	learn: 0.0293652	total: 46.8s	remaining: 14.1s
768:	learn: 0.0293349	total: 46.9s	remaining: 14.1s
769:	learn: 0.0292814	total: 47s	remaining: 14s
770:	learn: 0.0292484	total: 47s	remaining: 14s
771:	learn: 0.0292263	total: 47.1s	remaining: 13.9s
772:	learn: 0.0291994	total: 47.1s	remaining: 13.8s
773:	learn: 0.0291698	total: 47.2s	remaining: 13.8s
774:	learn: 0.0291616	total: 47.2s	remaining: 13.7s
775:	learn: 0.0291290	total: 47.3s	remaining: 13.7s
776:	learn: 0.0291081	total: 47.4s	remaining: 13.6s
777:	learn: 0.0291003	total: 47.4s	remaining: 13.5s
778:	learn: 0.0290828	total: 47.5s	remaining: 13.5s
779:	learn: 0.0290671	total: 47.5s	remaining: 13.4s
780:	learn: 0.0290225	total: 47.6s	remaining: 13.3s
781:	learn: 0.0290022	total: 47.7s	remaining: 13.3s
782:	learn: 0.0289453	total: 47.7s	remaining: 13.2s
783:	learn: 0.0289395	total: 47.8s	remaining: 13.2s
784:	learn: 0.0288931	total: 47.8s	remaining: 13.1s
785:	learn: 0.0288821	total: 47.9s	remaining: 13s
786:	learn: 0.0288725	total: 47.9s	remaining: 13s
787:	learn: 0.0288316	total: 48s	remaining: 12.9s
788:	learn: 0.0288094	total: 48.1s	remaining: 12.9s
789:	learn: 0.0287940	total: 48.1s	remaining: 12.8s
790:	learn: 0.0287743	total: 48.2s	remaining: 12.7s
791:	learn: 0.0287580	total: 48.3s	remaining: 12.7s
792:	learn: 0.0287179	total: 48.3s	remaining: 12.6s
793:	learn: 0.0286827	total: 48.4s	remaining: 12.6s
794:	learn: 0.0286620	total: 48.5s	remaining: 12.5s
795:	learn: 0.0286248	total: 48.5s	remaining: 12.4s
796:	learn: 0.0286188	total: 48.6s	remaining: 12.4s
797:	learn: 0.0286101	total: 48.7s	remaining: 12.3s
798:	learn: 0.0286036	total: 48.7s	remaining: 12.3s
799:	learn: 0.0285731	total: 48.8s	remaining: 12.2s
800:	learn: 0.0285253	total: 48.8s	remaining: 12.1s
801:	learn: 0.0284922	total: 48.9s	remaining: 12.1s
802:	learn: 0.0284438	total: 49s	remaining: 12s
803:	learn: 0.0284167	total: 49s	remaining: 12s
804:	learn: 0.0284091	total: 49.1s	remaining: 11.9s
805:	learn: 0.0283867	total: 49.1s	remaining: 11.8s
806:	learn: 0.0283554	total: 49.2s	remaining: 11.8s
807:	learn: 0.0283357	total: 49.3s	remaining: 11.7s
808:	learn: 0.0283245	total: 49.3s	remaining: 11.6s
809:	learn: 0.0283097	total: 49.4s	remaining: 11.6s
810:	learn: 0.0282817	total: 49.5s	remaining: 11.5s
811:	learn: 0.0282545	total: 49.5s	remaining: 11.5s
812:	learn: 0.0282239	total: 49.6s	remaining: 11.4s
813:	learn: 0.0281949	total: 49.6s	remaining: 11.3s
814:	learn: 0.0281466	total: 49.7s	remaining: 11.3s
815:	learn: 0.0281396	total: 49.7s	remaining: 11.2s
816:	learn: 0.0281171	total: 49.8s	remaining: 11.2s
817:	learn: 0.0280856	total: 49.9s	remaining: 11.1s
818:	learn: 0.0280447	total: 49.9s	remaining: 11s
819:	learn: 0.0280049	total: 50s	remaining: 11s
820:	learn: 0.0279862	total: 50.1s	remaining: 10.9s
821:	learn: 0.0279815	total: 50.1s	remaining: 10.9s
822:	learn: 0.0279661	total: 50.2s	remaining: 10.8s
823:	learn: 0.0279564	total: 50.2s	remaining: 10.7s
824:	learn: 0.0279282	total: 50.3s	remaining: 10.7s
825:	learn: 0.0278966	total: 50.4s	remaining: 10.6s
826:	learn: 0.0278695	total: 50.4s	remaining: 10.5s
827:	learn: 0.0278394	total: 50.5s	remaining: 10.5s
828:	learn: 0.0278141	total: 50.6s	remaining: 10.4s
829:	learn: 0.0277544	total: 50.6s	remaining: 10.4s
830:	learn: 0.0277108	total: 50.7s	remaining: 10.3s
831:	learn: 0.0276962	total: 50.8s	remaining: 10.3s
832:	learn: 0.0276768	total: 50.8s	remaining: 10.2s
833:	learn: 0.0276400	total: 50.9s	remaining: 10.1s
834:	learn: 0.0276325	total: 50.9s	remaining: 10.1s
835:	learn: 0.0275949	total: 51s	remaining: 10s
836:	learn: 0.0275841	total: 51.1s	remaining: 9.94s
837:	learn: 0.0275455	total: 51.1s	remaining: 9.88s
838:	learn: 0.0275059	total: 51.2s	remaining: 9.83s
839:	learn: 0.0274983	total: 51.3s	remaining: 9.76s
840:	learn: 0.0274948	total: 51.3s	remaining: 9.7s
841:	learn: 0.0274763	total: 51.4s	remaining: 9.64s
842:	learn: 0.0274520	total: 51.4s	remaining: 9.58s
843:	learn: 0.0274287	total: 51.5s	remaining: 9.52s
844:	learn: 0.0273869	total: 51.5s	remaining: 9.46s
845:	learn: 0.0273471	total: 51.6s	remaining: 9.39s
846:	learn: 0.0272965	total: 51.7s	remaining: 9.34s
847:	learn: 0.0272696	total: 51.8s	remaining: 9.28s
848:	learn: 0.0272295	total: 51.8s	remaining: 9.22s
849:	learn: 0.0272065	total: 51.9s	remaining: 9.16s
850:	learn: 0.0271902	total: 52s	remaining: 9.1s
851:	learn: 0.0271561	total: 52s	remaining: 9.04s
852:	learn: 0.0271316	total: 52.1s	remaining: 8.97s
853:	learn: 0.0271211	total: 52.1s	remaining: 8.91s
854:	learn: 0.0270982	total: 52.2s	remaining: 8.85s
855:	learn: 0.0270726	total: 52.2s	remaining: 8.79s
856:	learn: 0.0270376	total: 52.3s	remaining: 8.73s
857:	learn: 0.0270178	total: 52.4s	remaining: 8.67s
858:	learn: 0.0269915	total: 52.4s	remaining: 8.61s
859:	learn: 0.0269720	total: 52.5s	remaining: 8.55s
860:	learn: 0.0269257	total: 52.6s	remaining: 8.49s
861:	learn: 0.0268886	total: 52.6s	remaining: 8.43s
862:	learn: 0.0268453	total: 52.7s	remaining: 8.37s
863:	learn: 0.0268028	total: 52.8s	remaining: 8.3s
864:	learn: 0.0267707	total: 52.8s	remaining: 8.24s
865:	learn: 0.0267490	total: 52.9s	remaining: 8.18s
866:	learn: 0.0267007	total: 53s	remaining: 8.13s
867:	learn: 0.0266599	total: 53s	remaining: 8.06s
868:	learn: 0.0266335	total: 53.1s	remaining: 8s
869:	learn: 0.0266142	total: 53.1s	remaining: 7.94s
870:	learn: 0.0265786	total: 53.2s	remaining: 7.88s
871:	learn: 0.0265310	total: 53.3s	remaining: 7.82s
872:	learn: 0.0265262	total: 53.3s	remaining: 7.76s
873:	learn: 0.0265151	total: 53.4s	remaining: 7.7s
874:	learn: 0.0264768	total: 53.4s	remaining: 7.63s
875:	learn: 0.0264553	total: 53.5s	remaining: 7.58s
876:	learn: 0.0264412	total: 53.6s	remaining: 7.51s
877:	learn: 0.0264138	total: 53.6s	remaining: 7.45s
878:	learn: 0.0263742	total: 53.7s	remaining: 7.39s
879:	learn: 0.0263579	total: 53.8s	remaining: 7.33s
880:	learn: 0.0263405	total: 53.8s	remaining: 7.27s
881:	learn: 0.0263089	total: 53.9s	remaining: 7.21s
882:	learn: 0.0262753	total: 53.9s	remaining: 7.15s
883:	learn: 0.0262608	total: 54s	remaining: 7.08s
884:	learn: 0.0262116	total: 54.1s	remaining: 7.03s
885:	learn: 0.0261985	total: 54.1s	remaining: 6.96s
886:	learn: 0.0261505	total: 54.2s	remaining: 6.9s
887:	learn: 0.0261154	total: 54.3s	remaining: 6.84s
888:	learn: 0.0261085	total: 54.3s	remaining: 6.78s
889:	learn: 0.0260863	total: 54.4s	remaining: 6.72s
890:	learn: 0.0260754	total: 54.4s	remaining: 6.66s
891:	learn: 0.0260424	total: 54.5s	remaining: 6.6s
892:	learn: 0.0260266	total: 54.5s	remaining: 6.54s
893:	learn: 0.0260053	total: 54.6s	remaining: 6.47s
894:	learn: 0.0259687	total: 54.7s	remaining: 6.42s
895:	learn: 0.0259310	total: 54.8s	remaining: 6.36s
896:	learn: 0.0259012	total: 54.8s	remaining: 6.3s
897:	learn: 0.0258641	total: 54.9s	remaining: 6.24s
898:	learn: 0.0258284	total: 55s	remaining: 6.18s
899:	learn: 0.0258200	total: 55s	remaining: 6.11s
900:	learn: 0.0257979	total: 55.1s	remaining: 6.05s
901:	learn: 0.0257641	total: 55.2s	remaining: 5.99s
902:	learn: 0.0257610	total: 55.2s	remaining: 5.93s
903:	learn: 0.0257435	total: 55.3s	remaining: 5.87s
904:	learn: 0.0257032	total: 55.3s	remaining: 5.81s
905:	learn: 0.0256590	total: 55.4s	remaining: 5.75s
906:	learn: 0.0256056	total: 55.5s	remaining: 5.69s
907:	learn: 0.0255750	total: 55.5s	remaining: 5.63s
908:	learn: 0.0255551	total: 55.6s	remaining: 5.57s
909:	learn: 0.0255492	total: 55.7s	remaining: 5.5s
910:	learn: 0.0254860	total: 55.7s	remaining: 5.44s
911:	learn: 0.0254492	total: 55.8s	remaining: 5.38s
912:	learn: 0.0254190	total: 55.9s	remaining: 5.32s
913:	learn: 0.0253800	total: 55.9s	remaining: 5.26s
914:	learn: 0.0253681	total: 56s	remaining: 5.2s
915:	learn: 0.0253461	total: 56s	remaining: 5.14s
916:	learn: 0.0253275	total: 56.1s	remaining: 5.08s
917:	learn: 0.0253041	total: 56.2s	remaining: 5.01s
918:	learn: 0.0252481	total: 56.2s	remaining: 4.96s
919:	learn: 0.0252387	total: 56.3s	remaining: 4.89s
920:	learn: 0.0252107	total: 56.4s	remaining: 4.83s
921:	learn: 0.0251791	total: 56.4s	remaining: 4.77s
922:	learn: 0.0251718	total: 56.5s	remaining: 4.71s
923:	learn: 0.0251582	total: 56.5s	remaining: 4.65s
924:	learn: 0.0251404	total: 56.6s	remaining: 4.59s
925:	learn: 0.0251134	total: 56.7s	remaining: 4.53s
926:	learn: 0.0250812	total: 56.7s	remaining: 4.47s
927:	learn: 0.0250362	total: 56.8s	remaining: 4.41s
928:	learn: 0.0250161	total: 56.9s	remaining: 4.34s
929:	learn: 0.0249974	total: 56.9s	remaining: 4.28s
930:	learn: 0.0249790	total: 57s	remaining: 4.22s
931:	learn: 0.0249477	total: 57.1s	remaining: 4.16s
932:	learn: 0.0249317	total: 57.1s	remaining: 4.1s
933:	learn: 0.0249001	total: 57.2s	remaining: 4.04s
934:	learn: 0.0248885	total: 57.3s	remaining: 3.98s
935:	learn: 0.0248601	total: 57.3s	remaining: 3.92s
936:	learn: 0.0248141	total: 57.4s	remaining: 3.86s
937:	learn: 0.0247972	total: 57.5s	remaining: 3.8s
938:	learn: 0.0247723	total: 57.5s	remaining: 3.74s
939:	learn: 0.0247617	total: 57.6s	remaining: 3.67s
940:	learn: 0.0247540	total: 57.6s	remaining: 3.61s
941:	learn: 0.0247456	total: 57.7s	remaining: 3.55s
942:	learn: 0.0247084	total: 57.7s	remaining: 3.49s
943:	learn: 0.0246814	total: 57.8s	remaining: 3.43s
944:	learn: 0.0246637	total: 57.9s	remaining: 3.37s
945:	learn: 0.0246426	total: 57.9s	remaining: 3.31s
946:	learn: 0.0246356	total: 58s	remaining: 3.25s
947:	learn: 0.0245938	total: 58.1s	remaining: 3.18s
948:	learn: 0.0245792	total: 58.1s	remaining: 3.12s
949:	learn: 0.0245784	total: 58.2s	remaining: 3.06s
950:	learn: 0.0245669	total: 58.2s	remaining: 3s
951:	learn: 0.0245433	total: 58.3s	remaining: 2.94s
952:	learn: 0.0245134	total: 58.3s	remaining: 2.88s
953:	learn: 0.0245048	total: 58.4s	remaining: 2.82s
954:	learn: 0.0244880	total: 58.5s	remaining: 2.75s
955:	learn: 0.0244547	total: 58.5s	remaining: 2.69s
956:	learn: 0.0244538	total: 58.6s	remaining: 2.63s
957:	learn: 0.0244226	total: 58.6s	remaining: 2.57s
958:	learn: 0.0243939	total: 58.7s	remaining: 2.51s
959:	learn: 0.0243732	total: 58.8s	remaining: 2.45s
960:	learn: 0.0243672	total: 58.8s	remaining: 2.39s
961:	learn: 0.0243444	total: 58.9s	remaining: 2.33s
962:	learn: 0.0243128	total: 58.9s	remaining: 2.26s
963:	learn: 0.0242586	total: 59s	remaining: 2.2s
964:	learn: 0.0242476	total: 59.1s	remaining: 2.14s
965:	learn: 0.0242268	total: 59.1s	remaining: 2.08s
966:	learn: 0.0241832	total: 59.2s	remaining: 2.02s
967:	learn: 0.0241604	total: 59.3s	remaining: 1.96s
968:	learn: 0.0241518	total: 59.3s	remaining: 1.9s
969:	learn: 0.0241468	total: 59.4s	remaining: 1.83s
970:	learn: 0.0241116	total: 59.4s	remaining: 1.77s
971:	learn: 0.0240913	total: 59.5s	remaining: 1.71s
972:	learn: 0.0240713	total: 59.5s	remaining: 1.65s
973:	learn: 0.0240578	total: 59.6s	remaining: 1.59s
974:	learn: 0.0240443	total: 59.7s	remaining: 1.53s
975:	learn: 0.0240040	total: 59.7s	remaining: 1.47s
976:	learn: 0.0239970	total: 59.8s	remaining: 1.41s
977:	learn: 0.0239929	total: 59.8s	remaining: 1.34s
978:	learn: 0.0239928	total: 59.9s	remaining: 1.28s
979:	learn: 0.0239814	total: 59.9s	remaining: 1.22s
980:	learn: 0.0239763	total: 60s	remaining: 1.16s
981:	learn: 0.0239615	total: 1m	remaining: 1.1s
982:	learn: 0.0239614	total: 1m	remaining: 1.04s
983:	learn: 0.0239614	total: 1m	remaining: 978ms
984:	learn: 0.0239536	total: 1m	remaining: 916ms
985:	learn: 0.0239321	total: 1m	remaining: 855ms
986:	learn: 0.0239190	total: 1m	remaining: 794ms
987:	learn: 0.0238705	total: 1m	remaining: 733ms
988:	learn: 0.0238582	total: 1m	remaining: 672ms
989:	learn: 0.0238512	total: 1m	remaining: 611ms
990:	learn: 0.0238128	total: 1m	remaining: 550ms
991:	learn: 0.0237788	total: 1m	remaining: 489ms
992:	learn: 0.0237621	total: 1m	remaining: 428ms
993:	learn: 0.0237572	total: 1m	remaining: 367ms
994:	learn: 0.0237463	total: 1m	remaining: 306ms
995:	learn: 0.0237061	total: 1m	remaining: 245ms
996:	learn: 0.0236820	total: 1m	remaining: 183ms
997:	learn: 0.0236519	total: 1m 1s	remaining: 122ms
998:	learn: 0.0236496	total: 1m 1s	remaining: 61.1ms
999:	learn: 0.0236199	total: 1m 1s	remaining: 0us
Learning rate set to 0.046653
0:	learn: 0.5985820	total: 62.6ms	remaining: 1m 2s
1:	learn: 0.5173316	total: 131ms	remaining: 1m 5s
2:	learn: 0.4492102	total: 191ms	remaining: 1m 3s
3:	learn: 0.3904121	total: 259ms	remaining: 1m 4s
4:	learn: 0.3420826	total: 315ms	remaining: 1m 2s
5:	learn: 0.3012704	total: 389ms	remaining: 1m 4s
6:	learn: 0.2663768	total: 466ms	remaining: 1m 6s
7:	learn: 0.2365682	total: 533ms	remaining: 1m 6s
8:	learn: 0.2123325	total: 593ms	remaining: 1m 5s
9:	learn: 0.1916408	total: 656ms	remaining: 1m 4s
10:	learn: 0.1735519	total: 727ms	remaining: 1m 5s
11:	learn: 0.1588264	total: 795ms	remaining: 1m 5s
12:	learn: 0.1463341	total: 863ms	remaining: 1m 5s
13:	learn: 0.1355608	total: 927ms	remaining: 1m 5s
14:	learn: 0.1264740	total: 1.01s	remaining: 1m 6s
15:	learn: 0.1185384	total: 1.08s	remaining: 1m 6s
16:	learn: 0.1117647	total: 1.15s	remaining: 1m 6s
17:	learn: 0.1058788	total: 1.23s	remaining: 1m 6s
18:	learn: 0.1008190	total: 1.29s	remaining: 1m 6s
19:	learn: 0.0964954	total: 1.35s	remaining: 1m 6s
20:	learn: 0.0927134	total: 1.43s	remaining: 1m 6s
21:	learn: 0.0894623	total: 1.49s	remaining: 1m 6s
22:	learn: 0.0865246	total: 1.56s	remaining: 1m 6s
23:	learn: 0.0840954	total: 1.61s	remaining: 1m 5s
24:	learn: 0.0818885	total: 1.67s	remaining: 1m 5s
25:	learn: 0.0800008	total: 1.73s	remaining: 1m 4s
26:	learn: 0.0782675	total: 1.79s	remaining: 1m 4s
27:	learn: 0.0767592	total: 1.86s	remaining: 1m 4s
28:	learn: 0.0754017	total: 1.93s	remaining: 1m 4s
29:	learn: 0.0742309	total: 2.01s	remaining: 1m 4s
30:	learn: 0.0731431	total: 2.07s	remaining: 1m 4s
31:	learn: 0.0721526	total: 2.15s	remaining: 1m 5s
32:	learn: 0.0713148	total: 2.22s	remaining: 1m 5s
33:	learn: 0.0697075	total: 2.29s	remaining: 1m 5s
34:	learn: 0.0690022	total: 2.37s	remaining: 1m 5s
35:	learn: 0.0676832	total: 2.44s	remaining: 1m 5s
36:	learn: 0.0669113	total: 2.51s	remaining: 1m 5s
37:	learn: 0.0664909	total: 2.58s	remaining: 1m 5s
38:	learn: 0.0661312	total: 2.65s	remaining: 1m 5s
39:	learn: 0.0653984	total: 2.73s	remaining: 1m 5s
40:	learn: 0.0650612	total: 2.8s	remaining: 1m 5s
41:	learn: 0.0647145	total: 2.89s	remaining: 1m 5s
42:	learn: 0.0640206	total: 2.95s	remaining: 1m 5s
43:	learn: 0.0638028	total: 3.01s	remaining: 1m 5s
44:	learn: 0.0632807	total: 3.09s	remaining: 1m 5s
45:	learn: 0.0627798	total: 3.16s	remaining: 1m 5s
46:	learn: 0.0624388	total: 3.23s	remaining: 1m 5s
47:	learn: 0.0622782	total: 3.29s	remaining: 1m 5s
48:	learn: 0.0621461	total: 3.36s	remaining: 1m 5s
49:	learn: 0.0620047	total: 3.42s	remaining: 1m 5s
50:	learn: 0.0617786	total: 3.5s	remaining: 1m 5s
51:	learn: 0.0613692	total: 3.57s	remaining: 1m 5s
52:	learn: 0.0611064	total: 3.64s	remaining: 1m 4s
53:	learn: 0.0607663	total: 3.72s	remaining: 1m 5s
54:	learn: 0.0604297	total: 3.78s	remaining: 1m 5s
55:	learn: 0.0601436	total: 3.87s	remaining: 1m 5s
56:	learn: 0.0601041	total: 3.93s	remaining: 1m 5s
57:	learn: 0.0598662	total: 3.99s	remaining: 1m 4s
58:	learn: 0.0596443	total: 4.06s	remaining: 1m 4s
59:	learn: 0.0594530	total: 4.12s	remaining: 1m 4s
60:	learn: 0.0594005	total: 4.18s	remaining: 1m 4s
61:	learn: 0.0592093	total: 4.25s	remaining: 1m 4s
62:	learn: 0.0591765	total: 4.31s	remaining: 1m 4s
63:	learn: 0.0591322	total: 4.37s	remaining: 1m 3s
64:	learn: 0.0591008	total: 4.44s	remaining: 1m 3s
65:	learn: 0.0590602	total: 4.51s	remaining: 1m 3s
66:	learn: 0.0590184	total: 4.57s	remaining: 1m 3s
67:	learn: 0.0589933	total: 4.62s	remaining: 1m 3s
68:	learn: 0.0589674	total: 4.69s	remaining: 1m 3s
69:	learn: 0.0589441	total: 4.75s	remaining: 1m 3s
70:	learn: 0.0589258	total: 4.81s	remaining: 1m 2s
71:	learn: 0.0589037	total: 4.89s	remaining: 1m 3s
72:	learn: 0.0588703	total: 4.96s	remaining: 1m 2s
73:	learn: 0.0588388	total: 5.02s	remaining: 1m 2s
74:	learn: 0.0586668	total: 5.09s	remaining: 1m 2s
75:	learn: 0.0586229	total: 5.14s	remaining: 1m 2s
76:	learn: 0.0585349	total: 5.21s	remaining: 1m 2s
77:	learn: 0.0584942	total: 5.26s	remaining: 1m 2s
78:	learn: 0.0584792	total: 5.32s	remaining: 1m 2s
79:	learn: 0.0584591	total: 5.38s	remaining: 1m 1s
80:	learn: 0.0582941	total: 5.45s	remaining: 1m 1s
81:	learn: 0.0582821	total: 5.51s	remaining: 1m 1s
82:	learn: 0.0581383	total: 5.58s	remaining: 1m 1s
83:	learn: 0.0581028	total: 5.65s	remaining: 1m 1s
84:	learn: 0.0579750	total: 5.72s	remaining: 1m 1s
85:	learn: 0.0579611	total: 5.79s	remaining: 1m 1s
86:	learn: 0.0579480	total: 5.85s	remaining: 1m 1s
87:	learn: 0.0579304	total: 5.92s	remaining: 1m 1s
88:	learn: 0.0577588	total: 5.98s	remaining: 1m 1s
89:	learn: 0.0576974	total: 6.04s	remaining: 1m 1s
90:	learn: 0.0575734	total: 6.12s	remaining: 1m 1s
91:	learn: 0.0574317	total: 6.19s	remaining: 1m 1s
92:	learn: 0.0574182	total: 6.25s	remaining: 1m
93:	learn: 0.0574091	total: 6.3s	remaining: 1m
94:	learn: 0.0573436	total: 6.36s	remaining: 1m
95:	learn: 0.0573238	total: 6.42s	remaining: 1m
96:	learn: 0.0572655	total: 6.5s	remaining: 1m
97:	learn: 0.0572473	total: 6.56s	remaining: 1m
98:	learn: 0.0572359	total: 6.61s	remaining: 1m
99:	learn: 0.0571982	total: 6.67s	remaining: 1m
100:	learn: 0.0571744	total: 6.72s	remaining: 59.8s
101:	learn: 0.0571188	total: 6.78s	remaining: 59.7s
102:	learn: 0.0570546	total: 6.83s	remaining: 59.5s
103:	learn: 0.0570204	total: 6.91s	remaining: 59.5s
104:	learn: 0.0569844	total: 6.97s	remaining: 59.4s
105:	learn: 0.0569719	total: 7.03s	remaining: 59.3s
106:	learn: 0.0569311	total: 7.08s	remaining: 59.1s
107:	learn: 0.0568946	total: 7.14s	remaining: 59s
108:	learn: 0.0568306	total: 7.2s	remaining: 58.9s
109:	learn: 0.0567732	total: 7.28s	remaining: 58.9s
110:	learn: 0.0566366	total: 7.36s	remaining: 58.9s
111:	learn: 0.0565968	total: 7.41s	remaining: 58.8s
112:	learn: 0.0565246	total: 7.47s	remaining: 58.6s
113:	learn: 0.0564435	total: 7.54s	remaining: 58.6s
114:	learn: 0.0564369	total: 7.6s	remaining: 58.5s
115:	learn: 0.0563895	total: 7.66s	remaining: 58.3s
116:	learn: 0.0562423	total: 7.73s	remaining: 58.3s
117:	learn: 0.0561635	total: 7.79s	remaining: 58.2s
118:	learn: 0.0561058	total: 7.85s	remaining: 58.1s
119:	learn: 0.0560861	total: 7.9s	remaining: 57.9s
120:	learn: 0.0560688	total: 7.96s	remaining: 57.8s
121:	learn: 0.0560275	total: 8.02s	remaining: 57.8s
122:	learn: 0.0560089	total: 8.09s	remaining: 57.7s
123:	learn: 0.0559644	total: 8.15s	remaining: 57.6s
124:	learn: 0.0559125	total: 8.21s	remaining: 57.5s
125:	learn: 0.0558670	total: 8.28s	remaining: 57.4s
126:	learn: 0.0558503	total: 8.33s	remaining: 57.3s
127:	learn: 0.0557995	total: 8.41s	remaining: 57.3s
128:	learn: 0.0557808	total: 8.47s	remaining: 57.2s
129:	learn: 0.0556503	total: 8.55s	remaining: 57.3s
130:	learn: 0.0555918	total: 8.63s	remaining: 57.2s
131:	learn: 0.0555570	total: 8.68s	remaining: 57.1s
132:	learn: 0.0554510	total: 8.73s	remaining: 56.9s
133:	learn: 0.0554022	total: 8.79s	remaining: 56.8s
134:	learn: 0.0553656	total: 8.85s	remaining: 56.7s
135:	learn: 0.0553409	total: 8.92s	remaining: 56.6s
136:	learn: 0.0552956	total: 8.98s	remaining: 56.6s
137:	learn: 0.0552582	total: 9.04s	remaining: 56.5s
138:	learn: 0.0552058	total: 9.1s	remaining: 56.4s
139:	learn: 0.0551836	total: 9.15s	remaining: 56.2s
140:	learn: 0.0550885	total: 9.21s	remaining: 56.1s
141:	learn: 0.0550380	total: 9.29s	remaining: 56.1s
142:	learn: 0.0549921	total: 9.37s	remaining: 56.1s
143:	learn: 0.0549401	total: 9.44s	remaining: 56.1s
144:	learn: 0.0549180	total: 9.48s	remaining: 55.9s
145:	learn: 0.0549091	total: 9.54s	remaining: 55.8s
146:	learn: 0.0548739	total: 9.59s	remaining: 55.7s
147:	learn: 0.0548358	total: 9.65s	remaining: 55.5s
148:	learn: 0.0547776	total: 9.71s	remaining: 55.5s
149:	learn: 0.0547207	total: 9.78s	remaining: 55.4s
150:	learn: 0.0546665	total: 9.84s	remaining: 55.3s
151:	learn: 0.0546184	total: 9.9s	remaining: 55.2s
152:	learn: 0.0546031	total: 9.96s	remaining: 55.1s
153:	learn: 0.0545329	total: 10s	remaining: 55s
154:	learn: 0.0544325	total: 10.1s	remaining: 55s
155:	learn: 0.0543662	total: 10.2s	remaining: 55s
156:	learn: 0.0543412	total: 10.2s	remaining: 54.8s
157:	learn: 0.0543141	total: 10.3s	remaining: 54.8s
158:	learn: 0.0542405	total: 10.3s	remaining: 54.7s
159:	learn: 0.0541672	total: 10.4s	remaining: 54.6s
160:	learn: 0.0541283	total: 10.5s	remaining: 54.5s
161:	learn: 0.0540834	total: 10.5s	remaining: 54.4s
162:	learn: 0.0540394	total: 10.6s	remaining: 54.4s
163:	learn: 0.0540289	total: 10.6s	remaining: 54.2s
164:	learn: 0.0539746	total: 10.7s	remaining: 54.2s
165:	learn: 0.0539268	total: 10.8s	remaining: 54s
166:	learn: 0.0538945	total: 10.8s	remaining: 54s
167:	learn: 0.0538612	total: 10.9s	remaining: 53.9s
168:	learn: 0.0537804	total: 10.9s	remaining: 53.8s
169:	learn: 0.0536831	total: 11s	remaining: 53.8s
170:	learn: 0.0536275	total: 11.1s	remaining: 53.6s
171:	learn: 0.0536080	total: 11.1s	remaining: 53.5s
172:	learn: 0.0535602	total: 11.2s	remaining: 53.4s
173:	learn: 0.0535426	total: 11.2s	remaining: 53.3s
174:	learn: 0.0535012	total: 11.3s	remaining: 53.2s
175:	learn: 0.0534118	total: 11.3s	remaining: 53.1s
176:	learn: 0.0533516	total: 11.4s	remaining: 53s
177:	learn: 0.0533160	total: 11.5s	remaining: 52.9s
178:	learn: 0.0532226	total: 11.5s	remaining: 52.8s
179:	learn: 0.0531119	total: 11.6s	remaining: 52.8s
180:	learn: 0.0530844	total: 11.6s	remaining: 52.7s
181:	learn: 0.0530623	total: 11.7s	remaining: 52.5s
182:	learn: 0.0530183	total: 11.8s	remaining: 52.5s
183:	learn: 0.0529692	total: 11.8s	remaining: 52.4s
184:	learn: 0.0529354	total: 11.9s	remaining: 52.3s
185:	learn: 0.0528681	total: 11.9s	remaining: 52.2s
186:	learn: 0.0528399	total: 12s	remaining: 52.2s
187:	learn: 0.0527779	total: 12.1s	remaining: 52.1s
188:	learn: 0.0526919	total: 12.1s	remaining: 52s
189:	learn: 0.0526736	total: 12.2s	remaining: 51.9s
190:	learn: 0.0526321	total: 12.2s	remaining: 51.8s
191:	learn: 0.0526013	total: 12.3s	remaining: 51.7s
192:	learn: 0.0525607	total: 12.3s	remaining: 51.6s
193:	learn: 0.0524820	total: 12.4s	remaining: 51.6s
194:	learn: 0.0524009	total: 12.5s	remaining: 51.5s
195:	learn: 0.0523470	total: 12.5s	remaining: 51.4s
196:	learn: 0.0523080	total: 12.6s	remaining: 51.3s
197:	learn: 0.0522738	total: 12.6s	remaining: 51.2s
198:	learn: 0.0522375	total: 12.7s	remaining: 51.1s
199:	learn: 0.0521793	total: 12.8s	remaining: 51.1s
200:	learn: 0.0521414	total: 12.8s	remaining: 51s
201:	learn: 0.0520690	total: 12.9s	remaining: 50.9s
202:	learn: 0.0519651	total: 12.9s	remaining: 50.8s
203:	learn: 0.0518990	total: 13s	remaining: 50.8s
204:	learn: 0.0518105	total: 13.1s	remaining: 50.7s
205:	learn: 0.0517635	total: 13.1s	remaining: 50.6s
206:	learn: 0.0516953	total: 13.2s	remaining: 50.6s
207:	learn: 0.0516530	total: 13.3s	remaining: 50.5s
208:	learn: 0.0516183	total: 13.3s	remaining: 50.4s
209:	learn: 0.0515846	total: 13.4s	remaining: 50.3s
210:	learn: 0.0515513	total: 13.4s	remaining: 50.1s
211:	learn: 0.0514975	total: 13.5s	remaining: 50s
212:	learn: 0.0514686	total: 13.5s	remaining: 50s
213:	learn: 0.0514210	total: 13.6s	remaining: 49.9s
214:	learn: 0.0513600	total: 13.6s	remaining: 49.8s
215:	learn: 0.0513185	total: 13.7s	remaining: 49.7s
216:	learn: 0.0512539	total: 13.7s	remaining: 49.6s
217:	learn: 0.0511800	total: 13.8s	remaining: 49.5s
218:	learn: 0.0511541	total: 13.9s	remaining: 49.4s
219:	learn: 0.0511253	total: 13.9s	remaining: 49.3s
220:	learn: 0.0510897	total: 14s	remaining: 49.3s
221:	learn: 0.0510382	total: 14s	remaining: 49.2s
222:	learn: 0.0509665	total: 14.1s	remaining: 49.2s
223:	learn: 0.0509528	total: 14.2s	remaining: 49.1s
224:	learn: 0.0508745	total: 14.2s	remaining: 49s
225:	learn: 0.0508553	total: 14.3s	remaining: 48.9s
226:	learn: 0.0508159	total: 14.3s	remaining: 48.8s
227:	learn: 0.0507922	total: 14.4s	remaining: 48.8s
228:	learn: 0.0507763	total: 14.5s	remaining: 48.7s
229:	learn: 0.0507292	total: 14.5s	remaining: 48.6s
230:	learn: 0.0507058	total: 14.6s	remaining: 48.5s
231:	learn: 0.0506681	total: 14.6s	remaining: 48.4s
232:	learn: 0.0506527	total: 14.7s	remaining: 48.3s
233:	learn: 0.0506218	total: 14.7s	remaining: 48.2s
234:	learn: 0.0505735	total: 14.8s	remaining: 48.2s
235:	learn: 0.0505319	total: 14.8s	remaining: 48.1s
236:	learn: 0.0504852	total: 14.9s	remaining: 48s
237:	learn: 0.0504314	total: 15s	remaining: 47.9s
238:	learn: 0.0504257	total: 15s	remaining: 47.8s
239:	learn: 0.0504021	total: 15.1s	remaining: 47.8s
240:	learn: 0.0503661	total: 15.2s	remaining: 47.8s
241:	learn: 0.0503232	total: 15.2s	remaining: 47.6s
242:	learn: 0.0502704	total: 15.3s	remaining: 47.6s
243:	learn: 0.0502569	total: 15.3s	remaining: 47.5s
244:	learn: 0.0501624	total: 15.4s	remaining: 47.4s
245:	learn: 0.0500987	total: 15.5s	remaining: 47.4s
246:	learn: 0.0500813	total: 15.5s	remaining: 47.3s
247:	learn: 0.0500409	total: 15.6s	remaining: 47.2s
248:	learn: 0.0500309	total: 15.6s	remaining: 47.1s
249:	learn: 0.0499247	total: 15.7s	remaining: 47s
250:	learn: 0.0498919	total: 15.7s	remaining: 46.9s
251:	learn: 0.0498387	total: 15.8s	remaining: 46.9s
252:	learn: 0.0498121	total: 15.8s	remaining: 46.8s
253:	learn: 0.0497469	total: 15.9s	remaining: 46.7s
254:	learn: 0.0497145	total: 15.9s	remaining: 46.6s
255:	learn: 0.0496710	total: 16s	remaining: 46.5s
256:	learn: 0.0496509	total: 16.1s	remaining: 46.5s
257:	learn: 0.0495703	total: 16.1s	remaining: 46.4s
258:	learn: 0.0495639	total: 16.2s	remaining: 46.3s
259:	learn: 0.0495150	total: 16.2s	remaining: 46.2s
260:	learn: 0.0494956	total: 16.3s	remaining: 46.2s
261:	learn: 0.0494696	total: 16.4s	remaining: 46.1s
262:	learn: 0.0494499	total: 16.4s	remaining: 46s
263:	learn: 0.0493902	total: 16.5s	remaining: 46s
264:	learn: 0.0493777	total: 16.5s	remaining: 45.9s
265:	learn: 0.0493357	total: 16.6s	remaining: 45.8s
266:	learn: 0.0492910	total: 16.7s	remaining: 45.7s
267:	learn: 0.0492728	total: 16.7s	remaining: 45.6s
268:	learn: 0.0492329	total: 16.8s	remaining: 45.5s
269:	learn: 0.0491968	total: 16.8s	remaining: 45.5s
270:	learn: 0.0491469	total: 16.9s	remaining: 45.4s
271:	learn: 0.0490831	total: 16.9s	remaining: 45.3s
272:	learn: 0.0490623	total: 17s	remaining: 45.3s
273:	learn: 0.0490066	total: 17.1s	remaining: 45.2s
274:	learn: 0.0489836	total: 17.1s	remaining: 45.2s
275:	learn: 0.0489449	total: 17.2s	remaining: 45.1s
276:	learn: 0.0489203	total: 17.3s	remaining: 45s
277:	learn: 0.0488784	total: 17.3s	remaining: 45s
278:	learn: 0.0488171	total: 17.4s	remaining: 44.9s
279:	learn: 0.0487638	total: 17.4s	remaining: 44.8s
280:	learn: 0.0487174	total: 17.5s	remaining: 44.7s
281:	learn: 0.0486688	total: 17.6s	remaining: 44.7s
282:	learn: 0.0486421	total: 17.6s	remaining: 44.6s
283:	learn: 0.0485656	total: 17.7s	remaining: 44.5s
284:	learn: 0.0485473	total: 17.7s	remaining: 44.4s
285:	learn: 0.0484970	total: 17.8s	remaining: 44.4s
286:	learn: 0.0484652	total: 17.8s	remaining: 44.3s
287:	learn: 0.0483924	total: 17.9s	remaining: 44.3s
288:	learn: 0.0483502	total: 18s	remaining: 44.2s
289:	learn: 0.0482948	total: 18s	remaining: 44.1s
290:	learn: 0.0482756	total: 18.1s	remaining: 44s
291:	learn: 0.0482626	total: 18.1s	remaining: 44s
292:	learn: 0.0481943	total: 18.2s	remaining: 43.9s
293:	learn: 0.0481537	total: 18.3s	remaining: 43.8s
294:	learn: 0.0481314	total: 18.3s	remaining: 43.7s
295:	learn: 0.0481048	total: 18.4s	remaining: 43.6s
296:	learn: 0.0480656	total: 18.4s	remaining: 43.5s
297:	learn: 0.0480171	total: 18.5s	remaining: 43.5s
298:	learn: 0.0480134	total: 18.5s	remaining: 43.4s
299:	learn: 0.0479015	total: 18.6s	remaining: 43.4s
300:	learn: 0.0478755	total: 18.6s	remaining: 43.3s
301:	learn: 0.0478535	total: 18.7s	remaining: 43.2s
302:	learn: 0.0477742	total: 18.7s	remaining: 43.1s
303:	learn: 0.0477418	total: 18.8s	remaining: 43s
304:	learn: 0.0477041	total: 18.8s	remaining: 42.9s
305:	learn: 0.0476611	total: 18.9s	remaining: 42.9s
306:	learn: 0.0476357	total: 18.9s	remaining: 42.8s
307:	learn: 0.0475667	total: 19s	remaining: 42.7s
308:	learn: 0.0475653	total: 19s	remaining: 42.6s
309:	learn: 0.0475158	total: 19.1s	remaining: 42.5s
310:	learn: 0.0474756	total: 19.2s	remaining: 42.5s
311:	learn: 0.0474515	total: 19.2s	remaining: 42.4s
312:	learn: 0.0473875	total: 19.3s	remaining: 42.3s
313:	learn: 0.0473293	total: 19.4s	remaining: 42.3s
314:	learn: 0.0472997	total: 19.4s	remaining: 42.2s
315:	learn: 0.0472872	total: 19.5s	remaining: 42.1s
316:	learn: 0.0472610	total: 19.5s	remaining: 42.1s
317:	learn: 0.0472575	total: 19.6s	remaining: 42s
318:	learn: 0.0471730	total: 19.6s	remaining: 41.9s
319:	learn: 0.0470932	total: 19.7s	remaining: 41.9s
320:	learn: 0.0470736	total: 19.8s	remaining: 41.8s
321:	learn: 0.0470313	total: 19.8s	remaining: 41.8s
322:	learn: 0.0469431	total: 19.9s	remaining: 41.7s
323:	learn: 0.0469145	total: 19.9s	remaining: 41.6s
324:	learn: 0.0468395	total: 20s	remaining: 41.5s
325:	learn: 0.0468274	total: 20.1s	remaining: 41.5s
326:	learn: 0.0467565	total: 20.1s	remaining: 41.4s
327:	learn: 0.0467295	total: 20.2s	remaining: 41.4s
328:	learn: 0.0466917	total: 20.2s	remaining: 41.3s
329:	learn: 0.0466297	total: 20.3s	remaining: 41.2s
330:	learn: 0.0466174	total: 20.4s	remaining: 41.1s
331:	learn: 0.0465807	total: 20.4s	remaining: 41.1s
332:	learn: 0.0465274	total: 20.5s	remaining: 41s
333:	learn: 0.0464699	total: 20.6s	remaining: 41s
334:	learn: 0.0463839	total: 20.6s	remaining: 40.9s
335:	learn: 0.0463766	total: 20.7s	remaining: 40.9s
336:	learn: 0.0463547	total: 20.7s	remaining: 40.8s
337:	learn: 0.0463155	total: 20.8s	remaining: 40.7s
338:	learn: 0.0462347	total: 20.9s	remaining: 40.7s
339:	learn: 0.0461810	total: 20.9s	remaining: 40.6s
340:	learn: 0.0460932	total: 21s	remaining: 40.6s
341:	learn: 0.0460677	total: 21.1s	remaining: 40.5s
342:	learn: 0.0460043	total: 21.1s	remaining: 40.5s
343:	learn: 0.0459628	total: 21.2s	remaining: 40.4s
344:	learn: 0.0459090	total: 21.2s	remaining: 40.3s
345:	learn: 0.0458844	total: 21.3s	remaining: 40.3s
346:	learn: 0.0458173	total: 21.4s	remaining: 40.2s
347:	learn: 0.0457567	total: 21.4s	remaining: 40.1s
348:	learn: 0.0457258	total: 21.5s	remaining: 40.1s
349:	learn: 0.0457077	total: 21.5s	remaining: 40s
350:	learn: 0.0456648	total: 21.6s	remaining: 39.9s
351:	learn: 0.0456432	total: 21.6s	remaining: 39.8s
352:	learn: 0.0455908	total: 21.7s	remaining: 39.8s
353:	learn: 0.0455436	total: 21.8s	remaining: 39.7s
354:	learn: 0.0454859	total: 21.8s	remaining: 39.6s
355:	learn: 0.0454308	total: 21.9s	remaining: 39.6s
356:	learn: 0.0454227	total: 21.9s	remaining: 39.5s
357:	learn: 0.0453146	total: 22s	remaining: 39.4s
358:	learn: 0.0453010	total: 22s	remaining: 39.4s
359:	learn: 0.0452403	total: 22.1s	remaining: 39.3s
360:	learn: 0.0451879	total: 22.2s	remaining: 39.3s
361:	learn: 0.0451174	total: 22.3s	remaining: 39.2s
362:	learn: 0.0450556	total: 22.3s	remaining: 39.2s
363:	learn: 0.0450169	total: 22.4s	remaining: 39.1s
364:	learn: 0.0449544	total: 22.4s	remaining: 39s
365:	learn: 0.0449159	total: 22.5s	remaining: 39s
366:	learn: 0.0448556	total: 22.6s	remaining: 38.9s
367:	learn: 0.0448065	total: 22.6s	remaining: 38.8s
368:	learn: 0.0447589	total: 22.7s	remaining: 38.8s
369:	learn: 0.0447402	total: 22.7s	remaining: 38.7s
370:	learn: 0.0447086	total: 22.8s	remaining: 38.6s
371:	learn: 0.0446738	total: 22.8s	remaining: 38.5s
372:	learn: 0.0446498	total: 22.9s	remaining: 38.5s
373:	learn: 0.0446113	total: 22.9s	remaining: 38.4s
374:	learn: 0.0445783	total: 23s	remaining: 38.3s
375:	learn: 0.0445014	total: 23.1s	remaining: 38.3s
376:	learn: 0.0444903	total: 23.1s	remaining: 38.2s
377:	learn: 0.0444502	total: 23.2s	remaining: 38.1s
378:	learn: 0.0443792	total: 23.3s	remaining: 38.1s
379:	learn: 0.0442835	total: 23.3s	remaining: 38.1s
380:	learn: 0.0442476	total: 23.4s	remaining: 38s
381:	learn: 0.0442105	total: 23.5s	remaining: 37.9s
382:	learn: 0.0441894	total: 23.5s	remaining: 37.9s
383:	learn: 0.0441055	total: 23.6s	remaining: 37.8s
384:	learn: 0.0440756	total: 23.6s	remaining: 37.7s
385:	learn: 0.0440028	total: 23.7s	remaining: 37.7s
386:	learn: 0.0439991	total: 23.7s	remaining: 37.6s
387:	learn: 0.0439124	total: 23.8s	remaining: 37.6s
388:	learn: 0.0438365	total: 23.9s	remaining: 37.5s
389:	learn: 0.0438028	total: 23.9s	remaining: 37.4s
390:	learn: 0.0437870	total: 24s	remaining: 37.4s
391:	learn: 0.0437645	total: 24s	remaining: 37.3s
392:	learn: 0.0437349	total: 24.1s	remaining: 37.2s
393:	learn: 0.0436792	total: 24.2s	remaining: 37.2s
394:	learn: 0.0436558	total: 24.2s	remaining: 37.1s
395:	learn: 0.0436048	total: 24.3s	remaining: 37s
396:	learn: 0.0435613	total: 24.3s	remaining: 36.9s
397:	learn: 0.0435258	total: 24.4s	remaining: 36.9s
398:	learn: 0.0434138	total: 24.5s	remaining: 36.8s
399:	learn: 0.0433880	total: 24.5s	remaining: 36.8s
400:	learn: 0.0432676	total: 24.6s	remaining: 36.7s
401:	learn: 0.0432505	total: 24.6s	remaining: 36.6s
402:	learn: 0.0432188	total: 24.7s	remaining: 36.5s
403:	learn: 0.0431847	total: 24.7s	remaining: 36.5s
404:	learn: 0.0431626	total: 24.8s	remaining: 36.4s
405:	learn: 0.0430771	total: 24.8s	remaining: 36.3s
406:	learn: 0.0430326	total: 24.9s	remaining: 36.3s
407:	learn: 0.0429950	total: 25s	remaining: 36.2s
408:	learn: 0.0429454	total: 25s	remaining: 36.1s
409:	learn: 0.0428892	total: 25.1s	remaining: 36.1s
410:	learn: 0.0428304	total: 25.1s	remaining: 36s
411:	learn: 0.0428214	total: 25.2s	remaining: 36s
412:	learn: 0.0427921	total: 25.3s	remaining: 35.9s
413:	learn: 0.0427430	total: 25.3s	remaining: 35.8s
414:	learn: 0.0427203	total: 25.4s	remaining: 35.7s
415:	learn: 0.0426312	total: 25.4s	remaining: 35.7s
416:	learn: 0.0425710	total: 25.5s	remaining: 35.6s
417:	learn: 0.0425160	total: 25.6s	remaining: 35.6s
418:	learn: 0.0424546	total: 25.6s	remaining: 35.5s
419:	learn: 0.0423850	total: 25.7s	remaining: 35.5s
420:	learn: 0.0423553	total: 25.7s	remaining: 35.4s
421:	learn: 0.0422969	total: 25.8s	remaining: 35.3s
422:	learn: 0.0422459	total: 25.9s	remaining: 35.3s
423:	learn: 0.0421815	total: 25.9s	remaining: 35.2s
424:	learn: 0.0421152	total: 26s	remaining: 35.2s
425:	learn: 0.0420853	total: 26.1s	remaining: 35.1s
426:	learn: 0.0420424	total: 26.1s	remaining: 35s
427:	learn: 0.0420064	total: 26.2s	remaining: 35s
428:	learn: 0.0419473	total: 26.2s	remaining: 34.9s
429:	learn: 0.0418852	total: 26.3s	remaining: 34.9s
430:	learn: 0.0417925	total: 26.4s	remaining: 34.8s
431:	learn: 0.0417368	total: 26.4s	remaining: 34.8s
432:	learn: 0.0416929	total: 26.5s	remaining: 34.7s
433:	learn: 0.0416134	total: 26.6s	remaining: 34.7s
434:	learn: 0.0415729	total: 26.6s	remaining: 34.6s
435:	learn: 0.0414765	total: 26.7s	remaining: 34.5s
436:	learn: 0.0414583	total: 26.7s	remaining: 34.5s
437:	learn: 0.0414505	total: 26.8s	remaining: 34.4s
438:	learn: 0.0414169	total: 26.9s	remaining: 34.3s
439:	learn: 0.0413389	total: 26.9s	remaining: 34.3s
440:	learn: 0.0412830	total: 27s	remaining: 34.2s
441:	learn: 0.0412140	total: 27s	remaining: 34.1s
442:	learn: 0.0411930	total: 27.1s	remaining: 34.1s
443:	learn: 0.0411837	total: 27.1s	remaining: 34s
444:	learn: 0.0411709	total: 27.2s	remaining: 33.9s
445:	learn: 0.0411447	total: 27.3s	remaining: 33.9s
446:	learn: 0.0410851	total: 27.3s	remaining: 33.8s
447:	learn: 0.0410535	total: 27.4s	remaining: 33.7s
448:	learn: 0.0410071	total: 27.4s	remaining: 33.7s
449:	learn: 0.0409975	total: 27.5s	remaining: 33.6s
450:	learn: 0.0409268	total: 27.6s	remaining: 33.6s
451:	learn: 0.0408620	total: 27.6s	remaining: 33.5s
452:	learn: 0.0408114	total: 27.7s	remaining: 33.4s
453:	learn: 0.0408009	total: 27.7s	remaining: 33.4s
454:	learn: 0.0407919	total: 27.8s	remaining: 33.3s
455:	learn: 0.0407685	total: 27.8s	remaining: 33.2s
456:	learn: 0.0407615	total: 27.9s	remaining: 33.1s
457:	learn: 0.0407104	total: 27.9s	remaining: 33.1s
458:	learn: 0.0406526	total: 28s	remaining: 33s
459:	learn: 0.0406159	total: 28.1s	remaining: 32.9s
460:	learn: 0.0405408	total: 28.1s	remaining: 32.9s
461:	learn: 0.0405112	total: 28.2s	remaining: 32.8s
462:	learn: 0.0404648	total: 28.3s	remaining: 32.8s
463:	learn: 0.0404045	total: 28.3s	remaining: 32.7s
464:	learn: 0.0403864	total: 28.4s	remaining: 32.6s
465:	learn: 0.0403436	total: 28.4s	remaining: 32.6s
466:	learn: 0.0403264	total: 28.5s	remaining: 32.5s
467:	learn: 0.0403029	total: 28.5s	remaining: 32.4s
468:	learn: 0.0402103	total: 28.6s	remaining: 32.4s
469:	learn: 0.0402084	total: 28.7s	remaining: 32.3s
470:	learn: 0.0401648	total: 28.7s	remaining: 32.3s
471:	learn: 0.0400978	total: 28.8s	remaining: 32.2s
472:	learn: 0.0400392	total: 28.8s	remaining: 32.1s
473:	learn: 0.0399836	total: 28.9s	remaining: 32.1s
474:	learn: 0.0399253	total: 29s	remaining: 32s
475:	learn: 0.0398725	total: 29s	remaining: 32s
476:	learn: 0.0398248	total: 29.1s	remaining: 31.9s
477:	learn: 0.0398122	total: 29.1s	remaining: 31.8s
478:	learn: 0.0397666	total: 29.2s	remaining: 31.8s
479:	learn: 0.0397616	total: 29.2s	remaining: 31.7s
480:	learn: 0.0397487	total: 29.3s	remaining: 31.6s
481:	learn: 0.0396903	total: 29.4s	remaining: 31.5s
482:	learn: 0.0395977	total: 29.4s	remaining: 31.5s
483:	learn: 0.0395474	total: 29.5s	remaining: 31.4s
484:	learn: 0.0394978	total: 29.6s	remaining: 31.4s
485:	learn: 0.0394478	total: 29.6s	remaining: 31.3s
486:	learn: 0.0394084	total: 29.7s	remaining: 31.3s
487:	learn: 0.0393886	total: 29.7s	remaining: 31.2s
488:	learn: 0.0393556	total: 29.8s	remaining: 31.1s
489:	learn: 0.0393081	total: 29.8s	remaining: 31.1s
490:	learn: 0.0392374	total: 29.9s	remaining: 31s
491:	learn: 0.0391898	total: 30s	remaining: 30.9s
492:	learn: 0.0391847	total: 30s	remaining: 30.9s
493:	learn: 0.0391338	total: 30.1s	remaining: 30.8s
494:	learn: 0.0390855	total: 30.2s	remaining: 30.8s
495:	learn: 0.0389969	total: 30.2s	remaining: 30.7s
496:	learn: 0.0389370	total: 30.3s	remaining: 30.7s
497:	learn: 0.0388750	total: 30.4s	remaining: 30.6s
498:	learn: 0.0387994	total: 30.4s	remaining: 30.6s
499:	learn: 0.0387254	total: 30.5s	remaining: 30.5s
500:	learn: 0.0386387	total: 30.6s	remaining: 30.4s
501:	learn: 0.0386022	total: 30.6s	remaining: 30.4s
502:	learn: 0.0385690	total: 30.7s	remaining: 30.3s
503:	learn: 0.0385120	total: 30.7s	remaining: 30.2s
504:	learn: 0.0384767	total: 30.8s	remaining: 30.2s
505:	learn: 0.0384054	total: 30.9s	remaining: 30.1s
506:	learn: 0.0383606	total: 30.9s	remaining: 30.1s
507:	learn: 0.0383088	total: 31s	remaining: 30s
508:	learn: 0.0382171	total: 31.1s	remaining: 30s
509:	learn: 0.0381530	total: 31.1s	remaining: 29.9s
510:	learn: 0.0381250	total: 31.2s	remaining: 29.9s
511:	learn: 0.0381001	total: 31.3s	remaining: 29.8s
512:	learn: 0.0380557	total: 31.3s	remaining: 29.7s
513:	learn: 0.0380221	total: 31.4s	remaining: 29.7s
514:	learn: 0.0379921	total: 31.5s	remaining: 29.6s
515:	learn: 0.0379809	total: 31.5s	remaining: 29.6s
516:	learn: 0.0379717	total: 31.6s	remaining: 29.5s
517:	learn: 0.0379548	total: 31.6s	remaining: 29.4s
518:	learn: 0.0379174	total: 31.7s	remaining: 29.4s
519:	learn: 0.0379097	total: 31.7s	remaining: 29.3s
520:	learn: 0.0378473	total: 31.8s	remaining: 29.2s
521:	learn: 0.0378409	total: 31.9s	remaining: 29.2s
522:	learn: 0.0378328	total: 31.9s	remaining: 29.1s
523:	learn: 0.0378118	total: 32s	remaining: 29s
524:	learn: 0.0377997	total: 32s	remaining: 29s
525:	learn: 0.0377240	total: 32.1s	remaining: 28.9s
526:	learn: 0.0376931	total: 32.1s	remaining: 28.8s
527:	learn: 0.0376541	total: 32.2s	remaining: 28.8s
528:	learn: 0.0376368	total: 32.2s	remaining: 28.7s
529:	learn: 0.0376225	total: 32.3s	remaining: 28.6s
530:	learn: 0.0376138	total: 32.3s	remaining: 28.6s
531:	learn: 0.0375512	total: 32.4s	remaining: 28.5s
532:	learn: 0.0375024	total: 32.5s	remaining: 28.4s
533:	learn: 0.0374555	total: 32.5s	remaining: 28.4s
534:	learn: 0.0374174	total: 32.6s	remaining: 28.3s
535:	learn: 0.0374062	total: 32.6s	remaining: 28.2s
536:	learn: 0.0373948	total: 32.7s	remaining: 28.2s
537:	learn: 0.0373280	total: 32.8s	remaining: 28.1s
538:	learn: 0.0372738	total: 32.8s	remaining: 28.1s
539:	learn: 0.0372025	total: 32.9s	remaining: 28s
540:	learn: 0.0371638	total: 33s	remaining: 28s
541:	learn: 0.0371623	total: 33s	remaining: 27.9s
542:	learn: 0.0371276	total: 33.1s	remaining: 27.8s
543:	learn: 0.0370413	total: 33.1s	remaining: 27.8s
544:	learn: 0.0370181	total: 33.2s	remaining: 27.7s
545:	learn: 0.0369911	total: 33.3s	remaining: 27.7s
546:	learn: 0.0369204	total: 33.3s	remaining: 27.6s
547:	learn: 0.0368457	total: 33.4s	remaining: 27.6s
548:	learn: 0.0367675	total: 33.5s	remaining: 27.5s
549:	learn: 0.0367357	total: 33.5s	remaining: 27.4s
550:	learn: 0.0367091	total: 33.6s	remaining: 27.4s
551:	learn: 0.0366662	total: 33.6s	remaining: 27.3s
552:	learn: 0.0366344	total: 33.7s	remaining: 27.2s
553:	learn: 0.0365523	total: 33.8s	remaining: 27.2s
554:	learn: 0.0365459	total: 33.8s	remaining: 27.1s
555:	learn: 0.0365158	total: 33.9s	remaining: 27.1s
556:	learn: 0.0364846	total: 33.9s	remaining: 27s
557:	learn: 0.0364048	total: 34s	remaining: 26.9s
558:	learn: 0.0363978	total: 34.1s	remaining: 26.9s
559:	learn: 0.0363653	total: 34.1s	remaining: 26.8s
560:	learn: 0.0362987	total: 34.2s	remaining: 26.8s
561:	learn: 0.0362519	total: 34.3s	remaining: 26.7s
562:	learn: 0.0361898	total: 34.3s	remaining: 26.6s
563:	learn: 0.0361039	total: 34.4s	remaining: 26.6s
564:	learn: 0.0360257	total: 34.5s	remaining: 26.5s
565:	learn: 0.0360207	total: 34.5s	remaining: 26.5s
566:	learn: 0.0359908	total: 34.6s	remaining: 26.4s
567:	learn: 0.0359334	total: 34.7s	remaining: 26.4s
568:	learn: 0.0359173	total: 34.7s	remaining: 26.3s
569:	learn: 0.0359058	total: 34.8s	remaining: 26.2s
570:	learn: 0.0358902	total: 34.8s	remaining: 26.2s
571:	learn: 0.0358563	total: 34.9s	remaining: 26.1s
572:	learn: 0.0357997	total: 35s	remaining: 26s
573:	learn: 0.0357623	total: 35s	remaining: 26s
574:	learn: 0.0357157	total: 35.1s	remaining: 25.9s
575:	learn: 0.0357050	total: 35.1s	remaining: 25.9s
576:	learn: 0.0356646	total: 35.2s	remaining: 25.8s
577:	learn: 0.0355879	total: 35.3s	remaining: 25.8s
578:	learn: 0.0355383	total: 35.3s	remaining: 25.7s
579:	learn: 0.0354973	total: 35.4s	remaining: 25.6s
580:	learn: 0.0354367	total: 35.5s	remaining: 25.6s
581:	learn: 0.0353703	total: 35.6s	remaining: 25.5s
582:	learn: 0.0353234	total: 35.6s	remaining: 25.5s
583:	learn: 0.0352714	total: 35.7s	remaining: 25.4s
584:	learn: 0.0352603	total: 35.7s	remaining: 25.4s
585:	learn: 0.0352377	total: 35.8s	remaining: 25.3s
586:	learn: 0.0352052	total: 35.9s	remaining: 25.2s
587:	learn: 0.0351570	total: 35.9s	remaining: 25.2s
588:	learn: 0.0351365	total: 36s	remaining: 25.1s
589:	learn: 0.0351117	total: 36s	remaining: 25s
590:	learn: 0.0350788	total: 36.1s	remaining: 25s
591:	learn: 0.0350538	total: 36.2s	remaining: 24.9s
592:	learn: 0.0349838	total: 36.2s	remaining: 24.9s
593:	learn: 0.0349285	total: 36.3s	remaining: 24.8s
594:	learn: 0.0348947	total: 36.4s	remaining: 24.7s
595:	learn: 0.0348843	total: 36.4s	remaining: 24.7s
596:	learn: 0.0348569	total: 36.5s	remaining: 24.6s
597:	learn: 0.0347695	total: 36.5s	remaining: 24.6s
598:	learn: 0.0347263	total: 36.6s	remaining: 24.5s
599:	learn: 0.0347174	total: 36.6s	remaining: 24.4s
600:	learn: 0.0346471	total: 36.7s	remaining: 24.4s
601:	learn: 0.0346086	total: 36.8s	remaining: 24.3s
602:	learn: 0.0345841	total: 36.8s	remaining: 24.3s
603:	learn: 0.0345603	total: 36.9s	remaining: 24.2s
604:	learn: 0.0344764	total: 37s	remaining: 24.1s
605:	learn: 0.0344399	total: 37s	remaining: 24.1s
606:	learn: 0.0344014	total: 37.1s	remaining: 24s
607:	learn: 0.0343704	total: 37.1s	remaining: 23.9s
608:	learn: 0.0343513	total: 37.2s	remaining: 23.9s
609:	learn: 0.0343310	total: 37.2s	remaining: 23.8s
610:	learn: 0.0343106	total: 37.3s	remaining: 23.7s
611:	learn: 0.0342460	total: 37.3s	remaining: 23.7s
612:	learn: 0.0341998	total: 37.4s	remaining: 23.6s
613:	learn: 0.0341485	total: 37.4s	remaining: 23.5s
614:	learn: 0.0341300	total: 37.5s	remaining: 23.5s
615:	learn: 0.0340662	total: 37.6s	remaining: 23.4s
616:	learn: 0.0340367	total: 37.6s	remaining: 23.4s
617:	learn: 0.0339948	total: 37.7s	remaining: 23.3s
618:	learn: 0.0339590	total: 37.8s	remaining: 23.2s
619:	learn: 0.0339083	total: 37.8s	remaining: 23.2s
620:	learn: 0.0338593	total: 37.9s	remaining: 23.1s
621:	learn: 0.0338581	total: 37.9s	remaining: 23s
622:	learn: 0.0338179	total: 38s	remaining: 23s
623:	learn: 0.0338130	total: 38s	remaining: 22.9s
624:	learn: 0.0337832	total: 38.1s	remaining: 22.8s
625:	learn: 0.0337595	total: 38.1s	remaining: 22.8s
626:	learn: 0.0337012	total: 38.2s	remaining: 22.7s
627:	learn: 0.0336334	total: 38.3s	remaining: 22.7s
628:	learn: 0.0336011	total: 38.3s	remaining: 22.6s
629:	learn: 0.0335430	total: 38.4s	remaining: 22.6s
630:	learn: 0.0334922	total: 38.5s	remaining: 22.5s
631:	learn: 0.0334442	total: 38.5s	remaining: 22.4s
632:	learn: 0.0333960	total: 38.6s	remaining: 22.4s
633:	learn: 0.0333824	total: 38.6s	remaining: 22.3s
634:	learn: 0.0333581	total: 38.7s	remaining: 22.2s
635:	learn: 0.0333245	total: 38.8s	remaining: 22.2s
636:	learn: 0.0332779	total: 38.8s	remaining: 22.1s
637:	learn: 0.0332410	total: 38.9s	remaining: 22.1s
638:	learn: 0.0331780	total: 38.9s	remaining: 22s
639:	learn: 0.0331540	total: 39s	remaining: 21.9s
640:	learn: 0.0331051	total: 39.1s	remaining: 21.9s
641:	learn: 0.0330923	total: 39.1s	remaining: 21.8s
642:	learn: 0.0330630	total: 39.2s	remaining: 21.8s
643:	learn: 0.0329762	total: 39.3s	remaining: 21.7s
644:	learn: 0.0329524	total: 39.3s	remaining: 21.6s
645:	learn: 0.0329267	total: 39.4s	remaining: 21.6s
646:	learn: 0.0328759	total: 39.4s	remaining: 21.5s
647:	learn: 0.0328307	total: 39.5s	remaining: 21.4s
648:	learn: 0.0327744	total: 39.6s	remaining: 21.4s
649:	learn: 0.0327596	total: 39.6s	remaining: 21.3s
650:	learn: 0.0327240	total: 39.7s	remaining: 21.3s
651:	learn: 0.0327027	total: 39.7s	remaining: 21.2s
652:	learn: 0.0326758	total: 39.8s	remaining: 21.1s
653:	learn: 0.0326505	total: 39.8s	remaining: 21.1s
654:	learn: 0.0325814	total: 39.9s	remaining: 21s
655:	learn: 0.0325325	total: 40s	remaining: 21s
656:	learn: 0.0324997	total: 40s	remaining: 20.9s
657:	learn: 0.0324478	total: 40.1s	remaining: 20.8s
658:	learn: 0.0324392	total: 40.1s	remaining: 20.8s
659:	learn: 0.0324190	total: 40.2s	remaining: 20.7s
660:	learn: 0.0323440	total: 40.2s	remaining: 20.6s
661:	learn: 0.0323044	total: 40.3s	remaining: 20.6s
662:	learn: 0.0322880	total: 40.4s	remaining: 20.5s
663:	learn: 0.0322525	total: 40.4s	remaining: 20.5s
664:	learn: 0.0322166	total: 40.5s	remaining: 20.4s
665:	learn: 0.0321924	total: 40.6s	remaining: 20.3s
666:	learn: 0.0321404	total: 40.6s	remaining: 20.3s
667:	learn: 0.0321345	total: 40.7s	remaining: 20.2s
668:	learn: 0.0320820	total: 40.7s	remaining: 20.2s
669:	learn: 0.0320527	total: 40.8s	remaining: 20.1s
670:	learn: 0.0320344	total: 40.9s	remaining: 20s
671:	learn: 0.0320039	total: 40.9s	remaining: 20s
672:	learn: 0.0319712	total: 41s	remaining: 19.9s
673:	learn: 0.0319489	total: 41s	remaining: 19.8s
674:	learn: 0.0319128	total: 41.1s	remaining: 19.8s
675:	learn: 0.0318921	total: 41.1s	remaining: 19.7s
676:	learn: 0.0318835	total: 41.2s	remaining: 19.7s
677:	learn: 0.0318308	total: 41.3s	remaining: 19.6s
678:	learn: 0.0317854	total: 41.3s	remaining: 19.5s
679:	learn: 0.0317466	total: 41.4s	remaining: 19.5s
680:	learn: 0.0317276	total: 41.5s	remaining: 19.4s
681:	learn: 0.0317019	total: 41.5s	remaining: 19.4s
682:	learn: 0.0316769	total: 41.6s	remaining: 19.3s
683:	learn: 0.0316589	total: 41.6s	remaining: 19.2s
684:	learn: 0.0316255	total: 41.7s	remaining: 19.2s
685:	learn: 0.0315792	total: 41.8s	remaining: 19.1s
686:	learn: 0.0315164	total: 41.8s	remaining: 19.1s
687:	learn: 0.0314957	total: 41.9s	remaining: 19s
688:	learn: 0.0314729	total: 42s	remaining: 18.9s
689:	learn: 0.0314644	total: 42s	remaining: 18.9s
690:	learn: 0.0314420	total: 42.1s	remaining: 18.8s
691:	learn: 0.0314098	total: 42.1s	remaining: 18.8s
692:	learn: 0.0313772	total: 42.2s	remaining: 18.7s
693:	learn: 0.0313261	total: 42.3s	remaining: 18.6s
694:	learn: 0.0312876	total: 42.3s	remaining: 18.6s
695:	learn: 0.0312601	total: 42.4s	remaining: 18.5s
696:	learn: 0.0312395	total: 42.5s	remaining: 18.5s
697:	learn: 0.0311809	total: 42.5s	remaining: 18.4s
698:	learn: 0.0311781	total: 42.6s	remaining: 18.3s
699:	learn: 0.0311712	total: 42.6s	remaining: 18.3s
700:	learn: 0.0311475	total: 42.7s	remaining: 18.2s
701:	learn: 0.0310961	total: 42.8s	remaining: 18.2s
702:	learn: 0.0310749	total: 42.8s	remaining: 18.1s
703:	learn: 0.0310594	total: 42.9s	remaining: 18s
704:	learn: 0.0310438	total: 42.9s	remaining: 18s
705:	learn: 0.0309904	total: 43s	remaining: 17.9s
706:	learn: 0.0309827	total: 43.1s	remaining: 17.8s
707:	learn: 0.0309542	total: 43.1s	remaining: 17.8s
708:	learn: 0.0309378	total: 43.2s	remaining: 17.7s
709:	learn: 0.0309232	total: 43.2s	remaining: 17.7s
710:	learn: 0.0309089	total: 43.3s	remaining: 17.6s
711:	learn: 0.0308819	total: 43.3s	remaining: 17.5s
712:	learn: 0.0308784	total: 43.4s	remaining: 17.5s
713:	learn: 0.0308475	total: 43.4s	remaining: 17.4s
714:	learn: 0.0307808	total: 43.5s	remaining: 17.3s
715:	learn: 0.0307252	total: 43.6s	remaining: 17.3s
716:	learn: 0.0306798	total: 43.7s	remaining: 17.2s
717:	learn: 0.0306632	total: 43.7s	remaining: 17.2s
718:	learn: 0.0306394	total: 43.8s	remaining: 17.1s
719:	learn: 0.0306095	total: 43.8s	remaining: 17s
720:	learn: 0.0305709	total: 43.9s	remaining: 17s
721:	learn: 0.0305428	total: 44s	remaining: 16.9s
722:	learn: 0.0305055	total: 44s	remaining: 16.9s
723:	learn: 0.0304863	total: 44.1s	remaining: 16.8s
724:	learn: 0.0304333	total: 44.1s	remaining: 16.7s
725:	learn: 0.0304167	total: 44.2s	remaining: 16.7s
726:	learn: 0.0303732	total: 44.3s	remaining: 16.6s
727:	learn: 0.0303411	total: 44.3s	remaining: 16.6s
728:	learn: 0.0302890	total: 44.4s	remaining: 16.5s
729:	learn: 0.0302799	total: 44.4s	remaining: 16.4s
730:	learn: 0.0302242	total: 44.5s	remaining: 16.4s
731:	learn: 0.0302053	total: 44.6s	remaining: 16.3s
732:	learn: 0.0301846	total: 44.6s	remaining: 16.3s
733:	learn: 0.0301651	total: 44.7s	remaining: 16.2s
734:	learn: 0.0301264	total: 44.8s	remaining: 16.1s
735:	learn: 0.0300775	total: 44.8s	remaining: 16.1s
736:	learn: 0.0300620	total: 44.9s	remaining: 16s
737:	learn: 0.0299890	total: 44.9s	remaining: 16s
738:	learn: 0.0299744	total: 45s	remaining: 15.9s
739:	learn: 0.0299612	total: 45.1s	remaining: 15.8s
740:	learn: 0.0299299	total: 45.1s	remaining: 15.8s
741:	learn: 0.0299202	total: 45.2s	remaining: 15.7s
742:	learn: 0.0298787	total: 45.3s	remaining: 15.7s
743:	learn: 0.0298644	total: 45.3s	remaining: 15.6s
744:	learn: 0.0298457	total: 45.4s	remaining: 15.5s
745:	learn: 0.0298365	total: 45.4s	remaining: 15.5s
746:	learn: 0.0298264	total: 45.5s	remaining: 15.4s
747:	learn: 0.0297838	total: 45.6s	remaining: 15.3s
748:	learn: 0.0297631	total: 45.6s	remaining: 15.3s
749:	learn: 0.0297419	total: 45.7s	remaining: 15.2s
750:	learn: 0.0297090	total: 45.8s	remaining: 15.2s
751:	learn: 0.0296466	total: 45.8s	remaining: 15.1s
752:	learn: 0.0295994	total: 45.9s	remaining: 15.1s
753:	learn: 0.0295537	total: 46s	remaining: 15s
754:	learn: 0.0295343	total: 46s	remaining: 14.9s
755:	learn: 0.0295006	total: 46.1s	remaining: 14.9s
756:	learn: 0.0294704	total: 46.1s	remaining: 14.8s
757:	learn: 0.0294012	total: 46.2s	remaining: 14.7s
758:	learn: 0.0293799	total: 46.3s	remaining: 14.7s
759:	learn: 0.0293607	total: 46.3s	remaining: 14.6s
760:	learn: 0.0293355	total: 46.4s	remaining: 14.6s
761:	learn: 0.0292872	total: 46.4s	remaining: 14.5s
762:	learn: 0.0292629	total: 46.5s	remaining: 14.4s
763:	learn: 0.0292418	total: 46.5s	remaining: 14.4s
764:	learn: 0.0292093	total: 46.6s	remaining: 14.3s
765:	learn: 0.0291717	total: 46.7s	remaining: 14.3s
766:	learn: 0.0291368	total: 46.7s	remaining: 14.2s
767:	learn: 0.0291211	total: 46.8s	remaining: 14.1s
768:	learn: 0.0290763	total: 46.9s	remaining: 14.1s
769:	learn: 0.0290503	total: 46.9s	remaining: 14s
770:	learn: 0.0290295	total: 47s	remaining: 14s
771:	learn: 0.0289830	total: 47.1s	remaining: 13.9s
772:	learn: 0.0289437	total: 47.1s	remaining: 13.8s
773:	learn: 0.0288950	total: 47.2s	remaining: 13.8s
774:	learn: 0.0288760	total: 47.3s	remaining: 13.7s
775:	learn: 0.0288120	total: 47.3s	remaining: 13.7s
776:	learn: 0.0287898	total: 47.4s	remaining: 13.6s
777:	learn: 0.0287369	total: 47.5s	remaining: 13.5s
778:	learn: 0.0286822	total: 47.6s	remaining: 13.5s
779:	learn: 0.0286489	total: 47.6s	remaining: 13.4s
780:	learn: 0.0286389	total: 47.7s	remaining: 13.4s
781:	learn: 0.0285979	total: 47.7s	remaining: 13.3s
782:	learn: 0.0285788	total: 47.8s	remaining: 13.2s
783:	learn: 0.0285410	total: 47.9s	remaining: 13.2s
784:	learn: 0.0284821	total: 47.9s	remaining: 13.1s
785:	learn: 0.0284520	total: 48s	remaining: 13.1s
786:	learn: 0.0284267	total: 48s	remaining: 13s
787:	learn: 0.0284035	total: 48.1s	remaining: 12.9s
788:	learn: 0.0283821	total: 48.1s	remaining: 12.9s
789:	learn: 0.0283415	total: 48.2s	remaining: 12.8s
790:	learn: 0.0283231	total: 48.3s	remaining: 12.8s
791:	learn: 0.0283093	total: 48.3s	remaining: 12.7s
792:	learn: 0.0282770	total: 48.4s	remaining: 12.6s
793:	learn: 0.0282473	total: 48.4s	remaining: 12.6s
794:	learn: 0.0282316	total: 48.5s	remaining: 12.5s
795:	learn: 0.0282191	total: 48.6s	remaining: 12.4s
796:	learn: 0.0281821	total: 48.6s	remaining: 12.4s
797:	learn: 0.0281216	total: 48.7s	remaining: 12.3s
798:	learn: 0.0280940	total: 48.8s	remaining: 12.3s
799:	learn: 0.0280643	total: 48.8s	remaining: 12.2s
800:	learn: 0.0280549	total: 48.9s	remaining: 12.1s
801:	learn: 0.0280322	total: 48.9s	remaining: 12.1s
802:	learn: 0.0280194	total: 49s	remaining: 12s
803:	learn: 0.0279887	total: 49.1s	remaining: 12s
804:	learn: 0.0279529	total: 49.1s	remaining: 11.9s
805:	learn: 0.0279402	total: 49.2s	remaining: 11.8s
806:	learn: 0.0279270	total: 49.2s	remaining: 11.8s
807:	learn: 0.0279063	total: 49.3s	remaining: 11.7s
808:	learn: 0.0278907	total: 49.4s	remaining: 11.7s
809:	learn: 0.0278438	total: 49.4s	remaining: 11.6s
810:	learn: 0.0278262	total: 49.5s	remaining: 11.5s
811:	learn: 0.0278155	total: 49.5s	remaining: 11.5s
812:	learn: 0.0277873	total: 49.6s	remaining: 11.4s
813:	learn: 0.0277664	total: 49.6s	remaining: 11.3s
814:	learn: 0.0277364	total: 49.7s	remaining: 11.3s
815:	learn: 0.0277293	total: 49.8s	remaining: 11.2s
816:	learn: 0.0277093	total: 49.8s	remaining: 11.2s
817:	learn: 0.0277060	total: 49.9s	remaining: 11.1s
818:	learn: 0.0276939	total: 49.9s	remaining: 11s
819:	learn: 0.0276729	total: 50s	remaining: 11s
820:	learn: 0.0276398	total: 50.1s	remaining: 10.9s
821:	learn: 0.0276064	total: 50.1s	remaining: 10.9s
822:	learn: 0.0276011	total: 50.2s	remaining: 10.8s
823:	learn: 0.0275907	total: 50.2s	remaining: 10.7s
824:	learn: 0.0275779	total: 50.3s	remaining: 10.7s
825:	learn: 0.0275506	total: 50.4s	remaining: 10.6s
826:	learn: 0.0275368	total: 50.4s	remaining: 10.5s
827:	learn: 0.0274830	total: 50.5s	remaining: 10.5s
828:	learn: 0.0274754	total: 50.5s	remaining: 10.4s
829:	learn: 0.0274182	total: 50.6s	remaining: 10.4s
830:	learn: 0.0273944	total: 50.7s	remaining: 10.3s
831:	learn: 0.0273530	total: 50.7s	remaining: 10.2s
832:	learn: 0.0273503	total: 50.8s	remaining: 10.2s
833:	learn: 0.0273339	total: 50.8s	remaining: 10.1s
834:	learn: 0.0273019	total: 50.9s	remaining: 10.1s
835:	learn: 0.0272822	total: 51s	remaining: 10s
836:	learn: 0.0272585	total: 51s	remaining: 9.94s
837:	learn: 0.0272306	total: 51.1s	remaining: 9.88s
838:	learn: 0.0272190	total: 51.1s	remaining: 9.81s
839:	learn: 0.0271836	total: 51.2s	remaining: 9.75s
840:	learn: 0.0271805	total: 51.3s	remaining: 9.69s
841:	learn: 0.0271482	total: 51.3s	remaining: 9.63s
842:	learn: 0.0271228	total: 51.4s	remaining: 9.57s
843:	learn: 0.0270978	total: 51.4s	remaining: 9.51s
844:	learn: 0.0270687	total: 51.5s	remaining: 9.45s
845:	learn: 0.0270525	total: 51.6s	remaining: 9.38s
846:	learn: 0.0270306	total: 51.6s	remaining: 9.32s
847:	learn: 0.0270198	total: 51.7s	remaining: 9.26s
848:	learn: 0.0270056	total: 51.7s	remaining: 9.2s
849:	learn: 0.0269916	total: 51.8s	remaining: 9.14s
850:	learn: 0.0269662	total: 51.9s	remaining: 9.08s
851:	learn: 0.0269510	total: 51.9s	remaining: 9.02s
852:	learn: 0.0269342	total: 52s	remaining: 8.96s
853:	learn: 0.0269186	total: 52.1s	remaining: 8.9s
854:	learn: 0.0269078	total: 52.1s	remaining: 8.84s
855:	learn: 0.0268949	total: 52.2s	remaining: 8.78s
856:	learn: 0.0268846	total: 52.2s	remaining: 8.71s
857:	learn: 0.0268579	total: 52.3s	remaining: 8.65s
858:	learn: 0.0268392	total: 52.4s	remaining: 8.59s
859:	learn: 0.0267974	total: 52.4s	remaining: 8.53s
860:	learn: 0.0267527	total: 52.5s	remaining: 8.47s
861:	learn: 0.0267120	total: 52.6s	remaining: 8.41s
862:	learn: 0.0267075	total: 52.6s	remaining: 8.35s
863:	learn: 0.0266945	total: 52.7s	remaining: 8.29s
864:	learn: 0.0266798	total: 52.7s	remaining: 8.23s
865:	learn: 0.0266612	total: 52.8s	remaining: 8.17s
866:	learn: 0.0266219	total: 52.8s	remaining: 8.11s
867:	learn: 0.0266153	total: 52.9s	remaining: 8.04s
868:	learn: 0.0265824	total: 53s	remaining: 7.98s
869:	learn: 0.0265402	total: 53s	remaining: 7.92s
870:	learn: 0.0265088	total: 53.1s	remaining: 7.86s
871:	learn: 0.0264588	total: 53.2s	remaining: 7.8s
872:	learn: 0.0264275	total: 53.2s	remaining: 7.75s
873:	learn: 0.0264179	total: 53.3s	remaining: 7.68s
874:	learn: 0.0263735	total: 53.4s	remaining: 7.62s
875:	learn: 0.0263676	total: 53.4s	remaining: 7.56s
876:	learn: 0.0263486	total: 53.5s	remaining: 7.5s
877:	learn: 0.0263189	total: 53.5s	remaining: 7.44s
878:	learn: 0.0262735	total: 53.6s	remaining: 7.38s
879:	learn: 0.0262591	total: 53.6s	remaining: 7.32s
880:	learn: 0.0262263	total: 53.7s	remaining: 7.25s
881:	learn: 0.0262065	total: 53.8s	remaining: 7.2s
882:	learn: 0.0261969	total: 53.8s	remaining: 7.13s
883:	learn: 0.0261950	total: 53.9s	remaining: 7.07s
884:	learn: 0.0261852	total: 53.9s	remaining: 7.01s
885:	learn: 0.0261586	total: 54s	remaining: 6.95s
886:	learn: 0.0261461	total: 54s	remaining: 6.88s
887:	learn: 0.0261324	total: 54.1s	remaining: 6.82s
888:	learn: 0.0261239	total: 54.2s	remaining: 6.76s
889:	learn: 0.0261156	total: 54.2s	remaining: 6.7s
890:	learn: 0.0260992	total: 54.3s	remaining: 6.64s
891:	learn: 0.0260927	total: 54.3s	remaining: 6.58s
892:	learn: 0.0260683	total: 54.4s	remaining: 6.52s
893:	learn: 0.0260524	total: 54.4s	remaining: 6.46s
894:	learn: 0.0260190	total: 54.5s	remaining: 6.39s
895:	learn: 0.0259908	total: 54.6s	remaining: 6.33s
896:	learn: 0.0259498	total: 54.6s	remaining: 6.27s
897:	learn: 0.0259084	total: 54.7s	remaining: 6.21s
898:	learn: 0.0258922	total: 54.8s	remaining: 6.15s
899:	learn: 0.0258616	total: 54.8s	remaining: 6.09s
900:	learn: 0.0258357	total: 54.9s	remaining: 6.03s
901:	learn: 0.0258098	total: 55s	remaining: 5.97s
902:	learn: 0.0258074	total: 55s	remaining: 5.91s
903:	learn: 0.0257912	total: 55.1s	remaining: 5.85s
904:	learn: 0.0257696	total: 55.1s	remaining: 5.79s
905:	learn: 0.0257649	total: 55.2s	remaining: 5.72s
906:	learn: 0.0257337	total: 55.2s	remaining: 5.67s
907:	learn: 0.0257223	total: 55.3s	remaining: 5.6s
908:	learn: 0.0256933	total: 55.4s	remaining: 5.54s
909:	learn: 0.0256668	total: 55.4s	remaining: 5.48s
910:	learn: 0.0256553	total: 55.5s	remaining: 5.42s
911:	learn: 0.0256399	total: 55.5s	remaining: 5.36s
912:	learn: 0.0256175	total: 55.6s	remaining: 5.3s
913:	learn: 0.0256000	total: 55.7s	remaining: 5.24s
914:	learn: 0.0255877	total: 55.7s	remaining: 5.17s
915:	learn: 0.0255815	total: 55.8s	remaining: 5.11s
916:	learn: 0.0255506	total: 55.9s	remaining: 5.05s
917:	learn: 0.0255315	total: 55.9s	remaining: 4.99s
918:	learn: 0.0255209	total: 56s	remaining: 4.93s
919:	learn: 0.0254876	total: 56s	remaining: 4.87s
920:	learn: 0.0254597	total: 56.1s	remaining: 4.81s
921:	learn: 0.0254459	total: 56.2s	remaining: 4.75s
922:	learn: 0.0254261	total: 56.2s	remaining: 4.69s
923:	learn: 0.0254165	total: 56.3s	remaining: 4.63s
924:	learn: 0.0254016	total: 56.3s	remaining: 4.57s
925:	learn: 0.0253933	total: 56.4s	remaining: 4.51s
926:	learn: 0.0253657	total: 56.5s	remaining: 4.45s
927:	learn: 0.0253488	total: 56.5s	remaining: 4.38s
928:	learn: 0.0253219	total: 56.6s	remaining: 4.32s
929:	learn: 0.0252858	total: 56.6s	remaining: 4.26s
930:	learn: 0.0252447	total: 56.7s	remaining: 4.2s
931:	learn: 0.0252201	total: 56.8s	remaining: 4.14s
932:	learn: 0.0252068	total: 56.8s	remaining: 4.08s
933:	learn: 0.0252044	total: 56.9s	remaining: 4.02s
934:	learn: 0.0251946	total: 56.9s	remaining: 3.96s
935:	learn: 0.0251687	total: 57s	remaining: 3.9s
936:	learn: 0.0251498	total: 57s	remaining: 3.83s
937:	learn: 0.0251441	total: 57.1s	remaining: 3.77s
938:	learn: 0.0251251	total: 57.2s	remaining: 3.71s
939:	learn: 0.0250804	total: 57.2s	remaining: 3.65s
940:	learn: 0.0250231	total: 57.3s	remaining: 3.59s
941:	learn: 0.0249976	total: 57.4s	remaining: 3.53s
942:	learn: 0.0249630	total: 57.4s	remaining: 3.47s
943:	learn: 0.0249324	total: 57.5s	remaining: 3.41s
944:	learn: 0.0249065	total: 57.5s	remaining: 3.35s
945:	learn: 0.0248980	total: 57.6s	remaining: 3.29s
946:	learn: 0.0248872	total: 57.7s	remaining: 3.23s
947:	learn: 0.0248677	total: 57.7s	remaining: 3.17s
948:	learn: 0.0248617	total: 57.8s	remaining: 3.1s
949:	learn: 0.0248375	total: 57.8s	remaining: 3.04s
950:	learn: 0.0248168	total: 57.9s	remaining: 2.98s
951:	learn: 0.0248151	total: 57.9s	remaining: 2.92s
952:	learn: 0.0248015	total: 58s	remaining: 2.86s
953:	learn: 0.0247897	total: 58.1s	remaining: 2.8s
954:	learn: 0.0247890	total: 58.1s	remaining: 2.74s
955:	learn: 0.0247688	total: 58.1s	remaining: 2.68s
956:	learn: 0.0247564	total: 58.2s	remaining: 2.62s
957:	learn: 0.0247523	total: 58.3s	remaining: 2.55s
958:	learn: 0.0247495	total: 58.3s	remaining: 2.49s
959:	learn: 0.0247366	total: 58.4s	remaining: 2.43s
960:	learn: 0.0247230	total: 58.4s	remaining: 2.37s
961:	learn: 0.0246985	total: 58.5s	remaining: 2.31s
962:	learn: 0.0246745	total: 58.5s	remaining: 2.25s
963:	learn: 0.0246529	total: 58.6s	remaining: 2.19s
964:	learn: 0.0246442	total: 58.7s	remaining: 2.13s
965:	learn: 0.0246372	total: 58.7s	remaining: 2.07s
966:	learn: 0.0246222	total: 58.8s	remaining: 2s
967:	learn: 0.0246095	total: 58.8s	remaining: 1.94s
968:	learn: 0.0245870	total: 58.9s	remaining: 1.88s
969:	learn: 0.0245647	total: 59s	remaining: 1.82s
970:	learn: 0.0245331	total: 59s	remaining: 1.76s
971:	learn: 0.0245114	total: 59.1s	remaining: 1.7s
972:	learn: 0.0244715	total: 59.1s	remaining: 1.64s
973:	learn: 0.0244561	total: 59.2s	remaining: 1.58s
974:	learn: 0.0244389	total: 59.2s	remaining: 1.52s
975:	learn: 0.0244239	total: 59.3s	remaining: 1.46s
976:	learn: 0.0243812	total: 59.4s	remaining: 1.4s
977:	learn: 0.0243607	total: 59.4s	remaining: 1.34s
978:	learn: 0.0243321	total: 59.5s	remaining: 1.28s
979:	learn: 0.0243011	total: 59.6s	remaining: 1.22s
980:	learn: 0.0242851	total: 59.6s	remaining: 1.15s
981:	learn: 0.0242674	total: 59.7s	remaining: 1.09s
982:	learn: 0.0242570	total: 59.7s	remaining: 1.03s
983:	learn: 0.0242210	total: 59.8s	remaining: 972ms
984:	learn: 0.0242038	total: 59.8s	remaining: 911ms
985:	learn: 0.0241627	total: 59.9s	remaining: 851ms
986:	learn: 0.0241304	total: 60s	remaining: 790ms
987:	learn: 0.0241141	total: 1m	remaining: 729ms
988:	learn: 0.0241110	total: 1m	remaining: 668ms
989:	learn: 0.0240900	total: 1m	remaining: 608ms
990:	learn: 0.0240719	total: 1m	remaining: 547ms
991:	learn: 0.0240612	total: 1m	remaining: 486ms
992:	learn: 0.0240315	total: 1m	remaining: 425ms
993:	learn: 0.0240116	total: 1m	remaining: 365ms
994:	learn: 0.0239916	total: 1m	remaining: 304ms
995:	learn: 0.0239643	total: 1m	remaining: 243ms
996:	learn: 0.0239449	total: 1m	remaining: 182ms
997:	learn: 0.0239317	total: 1m	remaining: 122ms
998:	learn: 0.0239128	total: 1m	remaining: 60.8ms
999:	learn: 0.0239055	total: 1m	remaining: 0us
Learning rate set to 0.046653
0:	learn: 0.5985784	total: 62.5ms	remaining: 1m 2s
1:	learn: 0.5174625	total: 130ms	remaining: 1m 4s
2:	learn: 0.4491546	total: 192ms	remaining: 1m 3s
3:	learn: 0.3911268	total: 258ms	remaining: 1m 4s
4:	learn: 0.3426972	total: 312ms	remaining: 1m 2s
5:	learn: 0.3016545	total: 380ms	remaining: 1m 2s
6:	learn: 0.2666343	total: 466ms	remaining: 1m 6s
7:	learn: 0.2368907	total: 531ms	remaining: 1m 5s
8:	learn: 0.2126416	total: 590ms	remaining: 1m 5s
9:	learn: 0.1918373	total: 654ms	remaining: 1m 4s
10:	learn: 0.1734167	total: 721ms	remaining: 1m 4s
11:	learn: 0.1586886	total: 781ms	remaining: 1m 4s
12:	learn: 0.1462439	total: 854ms	remaining: 1m 4s
13:	learn: 0.1355409	total: 927ms	remaining: 1m 5s
14:	learn: 0.1264631	total: 1s	remaining: 1m 5s
15:	learn: 0.1184921	total: 1.08s	remaining: 1m 6s
16:	learn: 0.1117506	total: 1.15s	remaining: 1m 6s
17:	learn: 0.1059030	total: 1.22s	remaining: 1m 6s
18:	learn: 0.1008385	total: 1.28s	remaining: 1m 5s
19:	learn: 0.0965147	total: 1.34s	remaining: 1m 5s
20:	learn: 0.0927946	total: 1.41s	remaining: 1m 5s
21:	learn: 0.0895374	total: 1.48s	remaining: 1m 5s
22:	learn: 0.0865699	total: 1.54s	remaining: 1m 5s
23:	learn: 0.0841333	total: 1.59s	remaining: 1m 4s
24:	learn: 0.0819144	total: 1.65s	remaining: 1m 4s
25:	learn: 0.0793621	total: 1.71s	remaining: 1m 4s
26:	learn: 0.0776722	total: 1.77s	remaining: 1m 3s
27:	learn: 0.0761839	total: 1.84s	remaining: 1m 3s
28:	learn: 0.0748695	total: 1.91s	remaining: 1m 3s
29:	learn: 0.0736726	total: 1.98s	remaining: 1m 4s
30:	learn: 0.0726104	total: 2.05s	remaining: 1m 4s
31:	learn: 0.0716509	total: 2.13s	remaining: 1m 4s
32:	learn: 0.0708567	total: 2.19s	remaining: 1m 4s
33:	learn: 0.0691905	total: 2.27s	remaining: 1m 4s
34:	learn: 0.0685165	total: 2.34s	remaining: 1m 4s
35:	learn: 0.0679715	total: 2.39s	remaining: 1m 4s
36:	learn: 0.0670677	total: 2.47s	remaining: 1m 4s
37:	learn: 0.0665939	total: 2.55s	remaining: 1m 4s
38:	learn: 0.0662168	total: 2.6s	remaining: 1m 4s
39:	learn: 0.0653741	total: 2.68s	remaining: 1m 4s
40:	learn: 0.0643948	total: 2.76s	remaining: 1m 4s
41:	learn: 0.0641356	total: 2.83s	remaining: 1m 4s
42:	learn: 0.0639301	total: 2.88s	remaining: 1m 4s
43:	learn: 0.0636828	total: 2.95s	remaining: 1m 4s
44:	learn: 0.0634819	total: 3.02s	remaining: 1m 4s
45:	learn: 0.0628068	total: 3.09s	remaining: 1m 4s
46:	learn: 0.0621391	total: 3.17s	remaining: 1m 4s
47:	learn: 0.0619913	total: 3.23s	remaining: 1m 4s
48:	learn: 0.0617526	total: 3.32s	remaining: 1m 4s
49:	learn: 0.0616367	total: 3.37s	remaining: 1m 4s
50:	learn: 0.0611346	total: 3.45s	remaining: 1m 4s
51:	learn: 0.0606834	total: 3.53s	remaining: 1m 4s
52:	learn: 0.0604767	total: 3.59s	remaining: 1m 4s
53:	learn: 0.0601093	total: 3.66s	remaining: 1m 4s
54:	learn: 0.0600574	total: 3.71s	remaining: 1m 3s
55:	learn: 0.0596955	total: 3.78s	remaining: 1m 3s
56:	learn: 0.0596490	total: 3.83s	remaining: 1m 3s
57:	learn: 0.0592936	total: 3.91s	remaining: 1m 3s
58:	learn: 0.0591745	total: 3.97s	remaining: 1m 3s
59:	learn: 0.0589736	total: 4.03s	remaining: 1m 3s
60:	learn: 0.0589369	total: 4.08s	remaining: 1m 2s
61:	learn: 0.0588720	total: 4.14s	remaining: 1m 2s
62:	learn: 0.0588185	total: 4.2s	remaining: 1m 2s
63:	learn: 0.0587552	total: 4.26s	remaining: 1m 2s
64:	learn: 0.0587328	total: 4.33s	remaining: 1m 2s
65:	learn: 0.0587016	total: 4.38s	remaining: 1m 2s
66:	learn: 0.0586794	total: 4.43s	remaining: 1m 1s
67:	learn: 0.0586543	total: 4.49s	remaining: 1m 1s
68:	learn: 0.0586444	total: 4.54s	remaining: 1m 1s
69:	learn: 0.0585890	total: 4.6s	remaining: 1m 1s
70:	learn: 0.0585725	total: 4.65s	remaining: 1m
71:	learn: 0.0585421	total: 4.7s	remaining: 1m
72:	learn: 0.0585229	total: 4.76s	remaining: 1m
73:	learn: 0.0584872	total: 4.83s	remaining: 1m
74:	learn: 0.0584726	total: 4.95s	remaining: 1m 1s
75:	learn: 0.0584504	total: 5.06s	remaining: 1m 1s
76:	learn: 0.0584234	total: 5.16s	remaining: 1m 1s
77:	learn: 0.0581259	total: 5.26s	remaining: 1m 2s
78:	learn: 0.0581024	total: 5.33s	remaining: 1m 2s
79:	learn: 0.0580866	total: 5.37s	remaining: 1m 1s
80:	learn: 0.0579582	total: 5.44s	remaining: 1m 1s
81:	learn: 0.0579486	total: 5.5s	remaining: 1m 1s
82:	learn: 0.0579377	total: 5.57s	remaining: 1m 1s
83:	learn: 0.0577512	total: 5.63s	remaining: 1m 1s
84:	learn: 0.0575924	total: 5.7s	remaining: 1m 1s
85:	learn: 0.0575581	total: 5.75s	remaining: 1m 1s
86:	learn: 0.0575326	total: 5.81s	remaining: 1m
87:	learn: 0.0573216	total: 5.88s	remaining: 1m
88:	learn: 0.0573084	total: 5.93s	remaining: 1m
89:	learn: 0.0571411	total: 5.99s	remaining: 1m
90:	learn: 0.0569750	total: 6.06s	remaining: 1m
91:	learn: 0.0569604	total: 6.11s	remaining: 1m
92:	learn: 0.0569431	total: 6.18s	remaining: 1m
93:	learn: 0.0569089	total: 6.25s	remaining: 1m
94:	learn: 0.0568871	total: 6.31s	remaining: 1m
95:	learn: 0.0568457	total: 6.36s	remaining: 59.9s
96:	learn: 0.0568165	total: 6.43s	remaining: 59.9s
97:	learn: 0.0567778	total: 6.49s	remaining: 59.8s
98:	learn: 0.0567671	total: 6.56s	remaining: 59.7s
99:	learn: 0.0567231	total: 6.62s	remaining: 59.6s
100:	learn: 0.0565830	total: 6.69s	remaining: 59.6s
101:	learn: 0.0564167	total: 6.76s	remaining: 59.5s
102:	learn: 0.0564046	total: 6.83s	remaining: 59.5s
103:	learn: 0.0563812	total: 6.88s	remaining: 59.3s
104:	learn: 0.0562911	total: 6.95s	remaining: 59.3s
105:	learn: 0.0562668	total: 7.01s	remaining: 59.1s
106:	learn: 0.0562207	total: 7.07s	remaining: 59s
107:	learn: 0.0561492	total: 7.13s	remaining: 58.9s
108:	learn: 0.0560977	total: 7.19s	remaining: 58.8s
109:	learn: 0.0560034	total: 7.25s	remaining: 58.7s
110:	learn: 0.0559447	total: 7.31s	remaining: 58.6s
111:	learn: 0.0559212	total: 7.38s	remaining: 58.5s
112:	learn: 0.0558083	total: 7.45s	remaining: 58.4s
113:	learn: 0.0557029	total: 7.53s	remaining: 58.5s
114:	learn: 0.0556186	total: 7.61s	remaining: 58.6s
115:	learn: 0.0555255	total: 7.68s	remaining: 58.6s
116:	learn: 0.0554693	total: 7.75s	remaining: 58.5s
117:	learn: 0.0554202	total: 7.81s	remaining: 58.3s
118:	learn: 0.0553844	total: 7.86s	remaining: 58.2s
119:	learn: 0.0553723	total: 7.91s	remaining: 58s
120:	learn: 0.0553134	total: 7.97s	remaining: 57.9s
121:	learn: 0.0552598	total: 8.04s	remaining: 57.8s
122:	learn: 0.0551748	total: 8.1s	remaining: 57.7s
123:	learn: 0.0551356	total: 8.16s	remaining: 57.6s
124:	learn: 0.0550494	total: 8.22s	remaining: 57.6s
125:	learn: 0.0549946	total: 8.28s	remaining: 57.4s
126:	learn: 0.0549444	total: 8.34s	remaining: 57.4s
127:	learn: 0.0548695	total: 8.41s	remaining: 57.3s
128:	learn: 0.0548366	total: 8.48s	remaining: 57.3s
129:	learn: 0.0547804	total: 8.55s	remaining: 57.3s
130:	learn: 0.0547055	total: 8.63s	remaining: 57.2s
131:	learn: 0.0546908	total: 8.68s	remaining: 57s
132:	learn: 0.0546156	total: 8.74s	remaining: 57s
133:	learn: 0.0545957	total: 8.8s	remaining: 56.9s
134:	learn: 0.0545504	total: 8.85s	remaining: 56.7s
135:	learn: 0.0544939	total: 8.91s	remaining: 56.6s
136:	learn: 0.0544346	total: 8.97s	remaining: 56.5s
137:	learn: 0.0543818	total: 9.03s	remaining: 56.4s
138:	learn: 0.0543651	total: 9.09s	remaining: 56.3s
139:	learn: 0.0543148	total: 9.15s	remaining: 56.2s
140:	learn: 0.0542766	total: 9.21s	remaining: 56.1s
141:	learn: 0.0542566	total: 9.27s	remaining: 56s
142:	learn: 0.0542345	total: 9.32s	remaining: 55.9s
143:	learn: 0.0542038	total: 9.38s	remaining: 55.8s
144:	learn: 0.0541289	total: 9.47s	remaining: 55.8s
145:	learn: 0.0541049	total: 9.52s	remaining: 55.7s
146:	learn: 0.0540523	total: 9.59s	remaining: 55.7s
147:	learn: 0.0539553	total: 9.66s	remaining: 55.6s
148:	learn: 0.0538861	total: 9.73s	remaining: 55.6s
149:	learn: 0.0538608	total: 9.79s	remaining: 55.5s
150:	learn: 0.0537966	total: 9.86s	remaining: 55.4s
151:	learn: 0.0537206	total: 9.92s	remaining: 55.3s
152:	learn: 0.0536978	total: 9.98s	remaining: 55.2s
153:	learn: 0.0536707	total: 10s	remaining: 55.1s
154:	learn: 0.0536362	total: 10.1s	remaining: 55s
155:	learn: 0.0535929	total: 10.1s	remaining: 54.9s
156:	learn: 0.0535419	total: 10.2s	remaining: 54.7s
157:	learn: 0.0535006	total: 10.3s	remaining: 54.7s
158:	learn: 0.0534132	total: 10.3s	remaining: 54.6s
159:	learn: 0.0533802	total: 10.4s	remaining: 54.5s
160:	learn: 0.0533402	total: 10.4s	remaining: 54.4s
161:	learn: 0.0532953	total: 10.5s	remaining: 54.3s
162:	learn: 0.0532728	total: 10.6s	remaining: 54.2s
163:	learn: 0.0532348	total: 10.6s	remaining: 54.2s
164:	learn: 0.0532315	total: 10.7s	remaining: 54s
165:	learn: 0.0531150	total: 10.7s	remaining: 54s
166:	learn: 0.0530720	total: 10.8s	remaining: 53.9s
167:	learn: 0.0530092	total: 10.9s	remaining: 53.8s
168:	learn: 0.0529284	total: 11s	remaining: 53.9s
169:	learn: 0.0529052	total: 11s	remaining: 53.7s
170:	learn: 0.0528646	total: 11.1s	remaining: 53.6s
171:	learn: 0.0528443	total: 11.1s	remaining: 53.4s
172:	learn: 0.0527544	total: 11.2s	remaining: 53.4s
173:	learn: 0.0526699	total: 11.2s	remaining: 53.3s
174:	learn: 0.0526027	total: 11.3s	remaining: 53.2s
175:	learn: 0.0525700	total: 11.3s	remaining: 53.1s
176:	learn: 0.0525473	total: 11.4s	remaining: 53s
177:	learn: 0.0524863	total: 11.5s	remaining: 52.9s
178:	learn: 0.0524542	total: 11.5s	remaining: 52.8s
179:	learn: 0.0523909	total: 11.6s	remaining: 52.7s
180:	learn: 0.0523490	total: 11.7s	remaining: 52.7s
181:	learn: 0.0523318	total: 11.7s	remaining: 52.6s
182:	learn: 0.0522542	total: 11.8s	remaining: 52.6s
183:	learn: 0.0521791	total: 11.8s	remaining: 52.5s
184:	learn: 0.0521395	total: 11.9s	remaining: 52.5s
185:	learn: 0.0521046	total: 12s	remaining: 52.4s
186:	learn: 0.0520776	total: 12s	remaining: 52.3s
187:	learn: 0.0520394	total: 12.1s	remaining: 52.2s
188:	learn: 0.0520092	total: 12.1s	remaining: 52.1s
189:	learn: 0.0519395	total: 12.2s	remaining: 52.1s
190:	learn: 0.0519153	total: 12.3s	remaining: 52s
191:	learn: 0.0518655	total: 12.3s	remaining: 51.9s
192:	learn: 0.0518370	total: 12.4s	remaining: 51.8s
193:	learn: 0.0518065	total: 12.4s	remaining: 51.7s
194:	learn: 0.0517238	total: 12.5s	remaining: 51.7s
195:	learn: 0.0516777	total: 12.6s	remaining: 51.6s
196:	learn: 0.0516541	total: 12.6s	remaining: 51.5s
197:	learn: 0.0515947	total: 12.7s	remaining: 51.5s
198:	learn: 0.0515549	total: 12.8s	remaining: 51.5s
199:	learn: 0.0514771	total: 12.9s	remaining: 51.5s
200:	learn: 0.0514258	total: 12.9s	remaining: 51.4s
201:	learn: 0.0513660	total: 13s	remaining: 51.3s
202:	learn: 0.0512883	total: 13s	remaining: 51.2s
203:	learn: 0.0512778	total: 13.1s	remaining: 51.2s
204:	learn: 0.0512151	total: 13.2s	remaining: 51.1s
205:	learn: 0.0511794	total: 13.2s	remaining: 51s
206:	learn: 0.0511315	total: 13.3s	remaining: 50.9s
207:	learn: 0.0510906	total: 13.3s	remaining: 50.8s
208:	learn: 0.0510369	total: 13.4s	remaining: 50.7s
209:	learn: 0.0509763	total: 13.4s	remaining: 50.6s
210:	learn: 0.0509531	total: 13.5s	remaining: 50.5s
211:	learn: 0.0509327	total: 13.6s	remaining: 50.4s
212:	learn: 0.0509082	total: 13.6s	remaining: 50.4s
213:	learn: 0.0508898	total: 13.7s	remaining: 50.3s
214:	learn: 0.0508577	total: 13.8s	remaining: 50.2s
215:	learn: 0.0508180	total: 13.8s	remaining: 50.2s
216:	learn: 0.0507364	total: 13.9s	remaining: 50.1s
217:	learn: 0.0506961	total: 14s	remaining: 50.1s
218:	learn: 0.0506156	total: 14s	remaining: 50s
219:	learn: 0.0505983	total: 14.1s	remaining: 49.9s
220:	learn: 0.0505368	total: 14.2s	remaining: 49.9s
221:	learn: 0.0505155	total: 14.2s	remaining: 49.8s
222:	learn: 0.0504905	total: 14.3s	remaining: 49.7s
223:	learn: 0.0504532	total: 14.3s	remaining: 49.6s
224:	learn: 0.0504235	total: 14.4s	remaining: 49.5s
225:	learn: 0.0503524	total: 14.4s	remaining: 49.4s
226:	learn: 0.0503135	total: 14.5s	remaining: 49.3s
227:	learn: 0.0502749	total: 14.5s	remaining: 49.2s
228:	learn: 0.0501844	total: 14.6s	remaining: 49.2s
229:	learn: 0.0501551	total: 14.7s	remaining: 49.1s
230:	learn: 0.0501301	total: 14.7s	remaining: 49.1s
231:	learn: 0.0501022	total: 14.8s	remaining: 49s
232:	learn: 0.0500429	total: 14.9s	remaining: 48.9s
233:	learn: 0.0500052	total: 14.9s	remaining: 48.8s
234:	learn: 0.0499774	total: 15s	remaining: 48.7s
235:	learn: 0.0499091	total: 15s	remaining: 48.7s
236:	learn: 0.0498438	total: 15.1s	remaining: 48.6s
237:	learn: 0.0497486	total: 15.2s	remaining: 48.5s
238:	learn: 0.0496680	total: 15.2s	remaining: 48.5s
239:	learn: 0.0496398	total: 15.3s	remaining: 48.4s
240:	learn: 0.0495970	total: 15.3s	remaining: 48.3s
241:	learn: 0.0495170	total: 15.4s	remaining: 48.2s
242:	learn: 0.0494936	total: 15.4s	remaining: 48.1s
243:	learn: 0.0494371	total: 15.5s	remaining: 48s
244:	learn: 0.0493713	total: 15.6s	remaining: 48s
245:	learn: 0.0493679	total: 15.6s	remaining: 47.8s
246:	learn: 0.0493223	total: 15.7s	remaining: 47.8s
247:	learn: 0.0492972	total: 15.7s	remaining: 47.7s
248:	learn: 0.0492718	total: 15.8s	remaining: 47.6s
249:	learn: 0.0492432	total: 15.9s	remaining: 47.6s
250:	learn: 0.0492084	total: 15.9s	remaining: 47.5s
251:	learn: 0.0491451	total: 16s	remaining: 47.4s
252:	learn: 0.0490611	total: 16s	remaining: 47.4s
253:	learn: 0.0490474	total: 16.1s	remaining: 47.3s
254:	learn: 0.0490412	total: 16.2s	remaining: 47.2s
255:	learn: 0.0489748	total: 16.2s	remaining: 47.1s
256:	learn: 0.0489368	total: 16.3s	remaining: 47.1s
257:	learn: 0.0488984	total: 16.3s	remaining: 47s
258:	learn: 0.0488799	total: 16.4s	remaining: 47s
259:	learn: 0.0488464	total: 16.5s	remaining: 46.9s
260:	learn: 0.0487580	total: 16.5s	remaining: 46.8s
261:	learn: 0.0487471	total: 16.6s	remaining: 46.7s
262:	learn: 0.0487255	total: 16.6s	remaining: 46.6s
263:	learn: 0.0486876	total: 16.7s	remaining: 46.5s
264:	learn: 0.0486493	total: 16.8s	remaining: 46.5s
265:	learn: 0.0486141	total: 16.8s	remaining: 46.4s
266:	learn: 0.0485516	total: 16.9s	remaining: 46.3s
267:	learn: 0.0485267	total: 16.9s	remaining: 46.2s
268:	learn: 0.0484730	total: 17s	remaining: 46.2s
269:	learn: 0.0484099	total: 17s	remaining: 46.1s
270:	learn: 0.0483526	total: 17.1s	remaining: 46s
271:	learn: 0.0483067	total: 17.2s	remaining: 45.9s
272:	learn: 0.0482753	total: 17.2s	remaining: 45.8s
273:	learn: 0.0482361	total: 17.3s	remaining: 45.7s
274:	learn: 0.0481677	total: 17.3s	remaining: 45.7s
275:	learn: 0.0481502	total: 17.4s	remaining: 45.6s
276:	learn: 0.0481009	total: 17.4s	remaining: 45.5s
277:	learn: 0.0480765	total: 17.5s	remaining: 45.4s
278:	learn: 0.0480026	total: 17.5s	remaining: 45.3s
279:	learn: 0.0479572	total: 17.6s	remaining: 45.3s
280:	learn: 0.0478782	total: 17.7s	remaining: 45.2s
281:	learn: 0.0478074	total: 17.7s	remaining: 45.1s
282:	learn: 0.0477611	total: 17.8s	remaining: 45.1s
283:	learn: 0.0477327	total: 17.8s	remaining: 45s
284:	learn: 0.0477040	total: 17.9s	remaining: 44.9s
285:	learn: 0.0476327	total: 18s	remaining: 44.8s
286:	learn: 0.0475751	total: 18s	remaining: 44.8s
287:	learn: 0.0475232	total: 18.1s	remaining: 44.7s
288:	learn: 0.0474844	total: 18.1s	remaining: 44.6s
289:	learn: 0.0474210	total: 18.2s	remaining: 44.5s
290:	learn: 0.0473563	total: 18.3s	remaining: 44.5s
291:	learn: 0.0472900	total: 18.3s	remaining: 44.4s
292:	learn: 0.0472463	total: 18.4s	remaining: 44.3s
293:	learn: 0.0472072	total: 18.4s	remaining: 44.3s
294:	learn: 0.0472055	total: 18.5s	remaining: 44.2s
295:	learn: 0.0471420	total: 18.5s	remaining: 44.1s
296:	learn: 0.0471103	total: 18.6s	remaining: 44s
297:	learn: 0.0470723	total: 18.7s	remaining: 43.9s
298:	learn: 0.0470188	total: 18.7s	remaining: 43.9s
299:	learn: 0.0469819	total: 18.8s	remaining: 43.8s
300:	learn: 0.0469197	total: 18.8s	remaining: 43.7s
301:	learn: 0.0468570	total: 18.9s	remaining: 43.7s
302:	learn: 0.0468140	total: 19s	remaining: 43.7s
303:	learn: 0.0467581	total: 19s	remaining: 43.6s
304:	learn: 0.0467253	total: 19.1s	remaining: 43.5s
305:	learn: 0.0467107	total: 19.2s	remaining: 43.4s
306:	learn: 0.0466712	total: 19.2s	remaining: 43.4s
307:	learn: 0.0466668	total: 19.3s	remaining: 43.3s
308:	learn: 0.0466387	total: 19.3s	remaining: 43.2s
309:	learn: 0.0466036	total: 19.4s	remaining: 43.2s
310:	learn: 0.0465627	total: 19.5s	remaining: 43.1s
311:	learn: 0.0465112	total: 19.5s	remaining: 43.1s
312:	learn: 0.0464631	total: 19.6s	remaining: 43s
313:	learn: 0.0464430	total: 19.6s	remaining: 42.9s
314:	learn: 0.0464029	total: 19.7s	remaining: 42.9s
315:	learn: 0.0463463	total: 19.8s	remaining: 42.9s
316:	learn: 0.0463421	total: 19.9s	remaining: 42.8s
317:	learn: 0.0463284	total: 19.9s	remaining: 42.8s
318:	learn: 0.0462948	total: 20s	remaining: 42.7s
319:	learn: 0.0462569	total: 20.1s	remaining: 42.7s
320:	learn: 0.0461974	total: 20.1s	remaining: 42.6s
321:	learn: 0.0460889	total: 20.2s	remaining: 42.5s
322:	learn: 0.0460215	total: 20.3s	remaining: 42.5s
323:	learn: 0.0460049	total: 20.3s	remaining: 42.4s
324:	learn: 0.0460027	total: 20.4s	remaining: 42.3s
325:	learn: 0.0459373	total: 20.4s	remaining: 42.2s
326:	learn: 0.0459073	total: 20.5s	remaining: 42.1s
327:	learn: 0.0458962	total: 20.5s	remaining: 42.1s
328:	learn: 0.0458460	total: 20.6s	remaining: 42s
329:	learn: 0.0458009	total: 20.7s	remaining: 42s
330:	learn: 0.0457515	total: 20.7s	remaining: 41.9s
331:	learn: 0.0456991	total: 20.8s	remaining: 41.9s
332:	learn: 0.0456397	total: 20.9s	remaining: 41.8s
333:	learn: 0.0456049	total: 20.9s	remaining: 41.7s
334:	learn: 0.0455541	total: 21s	remaining: 41.7s
335:	learn: 0.0454967	total: 21s	remaining: 41.6s
336:	learn: 0.0454825	total: 21.1s	remaining: 41.5s
337:	learn: 0.0454535	total: 21.2s	remaining: 41.4s
338:	learn: 0.0454176	total: 21.2s	remaining: 41.3s
339:	learn: 0.0453717	total: 21.3s	remaining: 41.3s
340:	learn: 0.0453620	total: 21.3s	remaining: 41.2s
341:	learn: 0.0453270	total: 21.4s	remaining: 41.1s
342:	learn: 0.0452855	total: 21.4s	remaining: 41.1s
343:	learn: 0.0452456	total: 21.5s	remaining: 41s
344:	learn: 0.0452258	total: 21.5s	remaining: 40.9s
345:	learn: 0.0451696	total: 21.6s	remaining: 40.8s
346:	learn: 0.0451252	total: 21.7s	remaining: 40.8s
347:	learn: 0.0450704	total: 21.7s	remaining: 40.7s
348:	learn: 0.0449991	total: 21.8s	remaining: 40.7s
349:	learn: 0.0449393	total: 21.9s	remaining: 40.6s
350:	learn: 0.0449055	total: 21.9s	remaining: 40.5s
351:	learn: 0.0448890	total: 22s	remaining: 40.5s
352:	learn: 0.0448766	total: 22s	remaining: 40.4s
353:	learn: 0.0448050	total: 22.1s	remaining: 40.3s
354:	learn: 0.0447094	total: 22.2s	remaining: 40.3s
355:	learn: 0.0446518	total: 22.2s	remaining: 40.2s
356:	learn: 0.0445987	total: 22.3s	remaining: 40.2s
357:	learn: 0.0445702	total: 22.4s	remaining: 40.1s
358:	learn: 0.0445549	total: 22.4s	remaining: 40s
359:	learn: 0.0445407	total: 22.5s	remaining: 39.9s
360:	learn: 0.0445155	total: 22.5s	remaining: 39.9s
361:	learn: 0.0444851	total: 22.6s	remaining: 39.8s
362:	learn: 0.0444428	total: 22.6s	remaining: 39.7s
363:	learn: 0.0443873	total: 22.7s	remaining: 39.7s
364:	learn: 0.0443688	total: 22.8s	remaining: 39.6s
365:	learn: 0.0443217	total: 22.8s	remaining: 39.5s
366:	learn: 0.0442504	total: 22.9s	remaining: 39.5s
367:	learn: 0.0442054	total: 23s	remaining: 39.4s
368:	learn: 0.0441686	total: 23s	remaining: 39.4s
369:	learn: 0.0441344	total: 23.1s	remaining: 39.3s
370:	learn: 0.0440671	total: 23.1s	remaining: 39.2s
371:	learn: 0.0440064	total: 23.2s	remaining: 39.2s
372:	learn: 0.0438974	total: 23.3s	remaining: 39.2s
373:	learn: 0.0438830	total: 23.3s	remaining: 39.1s
374:	learn: 0.0438550	total: 23.4s	remaining: 39s
375:	learn: 0.0438395	total: 23.5s	remaining: 38.9s
376:	learn: 0.0438193	total: 23.5s	remaining: 38.9s
377:	learn: 0.0437541	total: 23.6s	remaining: 38.8s
378:	learn: 0.0437312	total: 23.6s	remaining: 38.7s
379:	learn: 0.0436476	total: 23.7s	remaining: 38.7s
380:	learn: 0.0435616	total: 23.8s	remaining: 38.6s
381:	learn: 0.0435394	total: 23.8s	remaining: 38.5s
382:	learn: 0.0435294	total: 23.9s	remaining: 38.5s
383:	learn: 0.0434548	total: 24s	remaining: 38.4s
384:	learn: 0.0434315	total: 24s	remaining: 38.4s
385:	learn: 0.0433919	total: 24.1s	remaining: 38.3s
386:	learn: 0.0433247	total: 24.1s	remaining: 38.2s
387:	learn: 0.0432756	total: 24.2s	remaining: 38.2s
388:	learn: 0.0432146	total: 24.3s	remaining: 38.1s
389:	learn: 0.0431689	total: 24.3s	remaining: 38.1s
390:	learn: 0.0431168	total: 24.4s	remaining: 38s
391:	learn: 0.0430786	total: 24.5s	remaining: 37.9s
392:	learn: 0.0429983	total: 24.5s	remaining: 37.9s
393:	learn: 0.0429257	total: 24.6s	remaining: 37.8s
394:	learn: 0.0428622	total: 24.6s	remaining: 37.8s
395:	learn: 0.0427929	total: 24.7s	remaining: 37.7s
396:	learn: 0.0427575	total: 24.8s	remaining: 37.6s
397:	learn: 0.0427455	total: 24.8s	remaining: 37.5s
398:	learn: 0.0427059	total: 24.9s	remaining: 37.5s
399:	learn: 0.0426329	total: 25s	remaining: 37.4s
400:	learn: 0.0425724	total: 25s	remaining: 37.4s
401:	learn: 0.0425436	total: 25.1s	remaining: 37.3s
402:	learn: 0.0424874	total: 25.1s	remaining: 37.2s
403:	learn: 0.0424522	total: 25.2s	remaining: 37.1s
404:	learn: 0.0424190	total: 25.2s	remaining: 37.1s
405:	learn: 0.0423850	total: 25.3s	remaining: 37s
406:	learn: 0.0423422	total: 25.3s	remaining: 36.9s
407:	learn: 0.0423154	total: 25.4s	remaining: 36.8s
408:	learn: 0.0423049	total: 25.4s	remaining: 36.8s
409:	learn: 0.0422892	total: 25.5s	remaining: 36.7s
410:	learn: 0.0422095	total: 25.6s	remaining: 36.6s
411:	learn: 0.0421682	total: 25.6s	remaining: 36.6s
412:	learn: 0.0421520	total: 25.7s	remaining: 36.5s
413:	learn: 0.0421213	total: 25.7s	remaining: 36.4s
414:	learn: 0.0420957	total: 25.8s	remaining: 36.4s
415:	learn: 0.0420800	total: 25.8s	remaining: 36.3s
416:	learn: 0.0420292	total: 25.9s	remaining: 36.2s
417:	learn: 0.0419841	total: 26s	remaining: 36.2s
418:	learn: 0.0419361	total: 26s	remaining: 36.1s
419:	learn: 0.0418808	total: 26.1s	remaining: 36s
420:	learn: 0.0418090	total: 26.2s	remaining: 36s
421:	learn: 0.0417416	total: 26.2s	remaining: 35.9s
422:	learn: 0.0417191	total: 26.3s	remaining: 35.8s
423:	learn: 0.0416688	total: 26.3s	remaining: 35.8s
424:	learn: 0.0415907	total: 26.4s	remaining: 35.7s
425:	learn: 0.0415483	total: 26.4s	remaining: 35.6s
426:	learn: 0.0415376	total: 26.5s	remaining: 35.6s
427:	learn: 0.0414983	total: 26.6s	remaining: 35.5s
428:	learn: 0.0414901	total: 26.6s	remaining: 35.4s
429:	learn: 0.0414655	total: 26.7s	remaining: 35.4s
430:	learn: 0.0413951	total: 26.7s	remaining: 35.3s
431:	learn: 0.0413070	total: 26.8s	remaining: 35.3s
432:	learn: 0.0412361	total: 26.9s	remaining: 35.2s
433:	learn: 0.0411996	total: 26.9s	remaining: 35.1s
434:	learn: 0.0411514	total: 27s	remaining: 35.1s
435:	learn: 0.0410926	total: 27.1s	remaining: 35s
436:	learn: 0.0410451	total: 27.1s	remaining: 34.9s
437:	learn: 0.0409714	total: 27.2s	remaining: 34.9s
438:	learn: 0.0409459	total: 27.2s	remaining: 34.8s
439:	learn: 0.0408667	total: 27.3s	remaining: 34.8s
440:	learn: 0.0408285	total: 27.4s	remaining: 34.7s
441:	learn: 0.0407919	total: 27.4s	remaining: 34.6s
442:	learn: 0.0407201	total: 27.5s	remaining: 34.5s
443:	learn: 0.0407056	total: 27.5s	remaining: 34.5s
444:	learn: 0.0406547	total: 27.6s	remaining: 34.4s
445:	learn: 0.0405754	total: 27.7s	remaining: 34.4s
446:	learn: 0.0405171	total: 27.7s	remaining: 34.3s
447:	learn: 0.0404873	total: 27.8s	remaining: 34.2s
448:	learn: 0.0404122	total: 27.9s	remaining: 34.2s
449:	learn: 0.0403617	total: 27.9s	remaining: 34.1s
450:	learn: 0.0403166	total: 28s	remaining: 34s
451:	learn: 0.0402755	total: 28s	remaining: 34s
452:	learn: 0.0402659	total: 28.1s	remaining: 33.9s
453:	learn: 0.0402456	total: 28.1s	remaining: 33.8s
454:	learn: 0.0401967	total: 28.2s	remaining: 33.8s
455:	learn: 0.0401529	total: 28.2s	remaining: 33.7s
456:	learn: 0.0400783	total: 28.3s	remaining: 33.6s
457:	learn: 0.0400325	total: 28.4s	remaining: 33.6s
458:	learn: 0.0399968	total: 28.4s	remaining: 33.5s
459:	learn: 0.0399289	total: 28.5s	remaining: 33.5s
460:	learn: 0.0398986	total: 28.6s	remaining: 33.4s
461:	learn: 0.0398581	total: 28.6s	remaining: 33.3s
462:	learn: 0.0397944	total: 28.7s	remaining: 33.3s
463:	learn: 0.0397190	total: 28.8s	remaining: 33.2s
464:	learn: 0.0397052	total: 28.8s	remaining: 33.2s
465:	learn: 0.0396530	total: 28.9s	remaining: 33.1s
466:	learn: 0.0395961	total: 28.9s	remaining: 33s
467:	learn: 0.0395730	total: 29s	remaining: 33s
468:	learn: 0.0395093	total: 29.1s	remaining: 32.9s
469:	learn: 0.0394827	total: 29.1s	remaining: 32.8s
470:	learn: 0.0394553	total: 29.2s	remaining: 32.8s
471:	learn: 0.0393666	total: 29.2s	remaining: 32.7s
472:	learn: 0.0393257	total: 29.3s	remaining: 32.6s
473:	learn: 0.0392737	total: 29.3s	remaining: 32.6s
474:	learn: 0.0392602	total: 29.4s	remaining: 32.5s
475:	learn: 0.0392391	total: 29.4s	remaining: 32.4s
476:	learn: 0.0392144	total: 29.5s	remaining: 32.3s
477:	learn: 0.0391754	total: 29.6s	remaining: 32.3s
478:	learn: 0.0391465	total: 29.6s	remaining: 32.2s
479:	learn: 0.0391327	total: 29.7s	remaining: 32.1s
480:	learn: 0.0390813	total: 29.7s	remaining: 32.1s
481:	learn: 0.0390206	total: 29.8s	remaining: 32s
482:	learn: 0.0390049	total: 29.8s	remaining: 31.9s
483:	learn: 0.0389747	total: 29.9s	remaining: 31.9s
484:	learn: 0.0389298	total: 29.9s	remaining: 31.8s
485:	learn: 0.0389208	total: 30s	remaining: 31.7s
486:	learn: 0.0388897	total: 30.1s	remaining: 31.7s
487:	learn: 0.0388615	total: 30.1s	remaining: 31.6s
488:	learn: 0.0388294	total: 30.2s	remaining: 31.5s
489:	learn: 0.0388114	total: 30.2s	remaining: 31.5s
490:	learn: 0.0387933	total: 30.3s	remaining: 31.4s
491:	learn: 0.0387563	total: 30.3s	remaining: 31.3s
492:	learn: 0.0387360	total: 30.4s	remaining: 31.3s
493:	learn: 0.0387232	total: 30.4s	remaining: 31.2s
494:	learn: 0.0386726	total: 30.5s	remaining: 31.1s
495:	learn: 0.0386555	total: 30.6s	remaining: 31.1s
496:	learn: 0.0385811	total: 30.6s	remaining: 31s
497:	learn: 0.0385268	total: 30.7s	remaining: 31s
498:	learn: 0.0385046	total: 30.8s	remaining: 30.9s
499:	learn: 0.0384929	total: 30.8s	remaining: 30.8s
500:	learn: 0.0384687	total: 30.9s	remaining: 30.8s
501:	learn: 0.0384300	total: 30.9s	remaining: 30.7s
502:	learn: 0.0383814	total: 31s	remaining: 30.7s
503:	learn: 0.0383074	total: 31.1s	remaining: 30.6s
504:	learn: 0.0382586	total: 31.2s	remaining: 30.5s
505:	learn: 0.0381811	total: 31.2s	remaining: 30.5s
506:	learn: 0.0381227	total: 31.3s	remaining: 30.4s
507:	learn: 0.0380295	total: 31.4s	remaining: 30.4s
508:	learn: 0.0380113	total: 31.4s	remaining: 30.3s
509:	learn: 0.0379971	total: 31.5s	remaining: 30.2s
510:	learn: 0.0379204	total: 31.5s	remaining: 30.2s
511:	learn: 0.0378594	total: 31.6s	remaining: 30.1s
512:	learn: 0.0378465	total: 31.7s	remaining: 30.1s
513:	learn: 0.0378076	total: 31.7s	remaining: 30s
514:	learn: 0.0377825	total: 31.8s	remaining: 29.9s
515:	learn: 0.0377246	total: 31.8s	remaining: 29.9s
516:	learn: 0.0376858	total: 31.9s	remaining: 29.8s
517:	learn: 0.0375982	total: 32s	remaining: 29.7s
518:	learn: 0.0375649	total: 32s	remaining: 29.7s
519:	learn: 0.0374781	total: 32.1s	remaining: 29.6s
520:	learn: 0.0374383	total: 32.2s	remaining: 29.6s
521:	learn: 0.0373837	total: 32.2s	remaining: 29.5s
522:	learn: 0.0373683	total: 32.3s	remaining: 29.5s
523:	learn: 0.0373589	total: 32.3s	remaining: 29.4s
524:	learn: 0.0373267	total: 32.4s	remaining: 29.3s
525:	learn: 0.0373022	total: 32.5s	remaining: 29.2s
526:	learn: 0.0372892	total: 32.5s	remaining: 29.2s
527:	learn: 0.0372495	total: 32.6s	remaining: 29.1s
528:	learn: 0.0371877	total: 32.6s	remaining: 29.1s
529:	learn: 0.0371243	total: 32.7s	remaining: 29s
530:	learn: 0.0371001	total: 32.7s	remaining: 28.9s
531:	learn: 0.0370601	total: 32.8s	remaining: 28.9s
532:	learn: 0.0370206	total: 32.9s	remaining: 28.8s
533:	learn: 0.0369368	total: 32.9s	remaining: 28.7s
534:	learn: 0.0369018	total: 33s	remaining: 28.7s
535:	learn: 0.0368711	total: 33.1s	remaining: 28.6s
536:	learn: 0.0368123	total: 33.1s	remaining: 28.6s
537:	learn: 0.0367486	total: 33.2s	remaining: 28.5s
538:	learn: 0.0367164	total: 33.2s	remaining: 28.4s
539:	learn: 0.0366669	total: 33.3s	remaining: 28.4s
540:	learn: 0.0366310	total: 33.4s	remaining: 28.3s
541:	learn: 0.0366131	total: 33.4s	remaining: 28.2s
542:	learn: 0.0365161	total: 33.5s	remaining: 28.2s
543:	learn: 0.0364836	total: 33.6s	remaining: 28.1s
544:	learn: 0.0364723	total: 33.6s	remaining: 28.1s
545:	learn: 0.0364360	total: 33.7s	remaining: 28s
546:	learn: 0.0363873	total: 33.8s	remaining: 28s
547:	learn: 0.0363400	total: 33.8s	remaining: 27.9s
548:	learn: 0.0362655	total: 33.9s	remaining: 27.8s
549:	learn: 0.0362194	total: 34s	remaining: 27.8s
550:	learn: 0.0361840	total: 34s	remaining: 27.7s
551:	learn: 0.0361258	total: 34.1s	remaining: 27.7s
552:	learn: 0.0361146	total: 34.1s	remaining: 27.6s
553:	learn: 0.0360557	total: 34.2s	remaining: 27.5s
554:	learn: 0.0360212	total: 34.3s	remaining: 27.5s
555:	learn: 0.0359791	total: 34.3s	remaining: 27.4s
556:	learn: 0.0359397	total: 34.4s	remaining: 27.3s
557:	learn: 0.0358647	total: 34.4s	remaining: 27.3s
558:	learn: 0.0358296	total: 34.5s	remaining: 27.2s
559:	learn: 0.0357517	total: 34.6s	remaining: 27.2s
560:	learn: 0.0357084	total: 34.6s	remaining: 27.1s
561:	learn: 0.0357040	total: 34.7s	remaining: 27s
562:	learn: 0.0356898	total: 34.7s	remaining: 27s
563:	learn: 0.0356277	total: 34.8s	remaining: 26.9s
564:	learn: 0.0355821	total: 34.9s	remaining: 26.8s
565:	learn: 0.0355452	total: 34.9s	remaining: 26.8s
566:	learn: 0.0355292	total: 35s	remaining: 26.7s
567:	learn: 0.0355219	total: 35s	remaining: 26.6s
568:	learn: 0.0354993	total: 35.1s	remaining: 26.6s
569:	learn: 0.0354583	total: 35.2s	remaining: 26.5s
570:	learn: 0.0354022	total: 35.2s	remaining: 26.5s
571:	learn: 0.0353496	total: 35.3s	remaining: 26.4s
572:	learn: 0.0352968	total: 35.4s	remaining: 26.3s
573:	learn: 0.0352559	total: 35.4s	remaining: 26.3s
574:	learn: 0.0351879	total: 35.5s	remaining: 26.2s
575:	learn: 0.0351813	total: 35.5s	remaining: 26.2s
576:	learn: 0.0351098	total: 35.6s	remaining: 26.1s
577:	learn: 0.0350913	total: 35.7s	remaining: 26s
578:	learn: 0.0350241	total: 35.7s	remaining: 26s
579:	learn: 0.0349892	total: 35.8s	remaining: 25.9s
580:	learn: 0.0349728	total: 35.9s	remaining: 25.9s
581:	learn: 0.0349579	total: 35.9s	remaining: 25.8s
582:	learn: 0.0349141	total: 36s	remaining: 25.7s
583:	learn: 0.0348524	total: 36s	remaining: 25.7s
584:	learn: 0.0348247	total: 36.1s	remaining: 25.6s
585:	learn: 0.0348009	total: 36.2s	remaining: 25.6s
586:	learn: 0.0347733	total: 36.2s	remaining: 25.5s
587:	learn: 0.0347195	total: 36.3s	remaining: 25.4s
588:	learn: 0.0346885	total: 36.4s	remaining: 25.4s
589:	learn: 0.0346633	total: 36.4s	remaining: 25.3s
590:	learn: 0.0346095	total: 36.5s	remaining: 25.2s
591:	learn: 0.0345859	total: 36.5s	remaining: 25.2s
592:	learn: 0.0345647	total: 36.6s	remaining: 25.1s
593:	learn: 0.0345363	total: 36.6s	remaining: 25s
594:	learn: 0.0345205	total: 36.7s	remaining: 25s
595:	learn: 0.0344363	total: 36.8s	remaining: 24.9s
596:	learn: 0.0343832	total: 36.8s	remaining: 24.9s
597:	learn: 0.0343400	total: 36.9s	remaining: 24.8s
598:	learn: 0.0343005	total: 37s	remaining: 24.7s
599:	learn: 0.0342423	total: 37s	remaining: 24.7s
600:	learn: 0.0342158	total: 37.1s	remaining: 24.6s
601:	learn: 0.0341780	total: 37.2s	remaining: 24.6s
602:	learn: 0.0341137	total: 37.2s	remaining: 24.5s
603:	learn: 0.0341037	total: 37.3s	remaining: 24.5s
604:	learn: 0.0340488	total: 37.4s	remaining: 24.4s
605:	learn: 0.0339885	total: 37.4s	remaining: 24.3s
606:	learn: 0.0339149	total: 37.5s	remaining: 24.3s
607:	learn: 0.0339045	total: 37.6s	remaining: 24.2s
608:	learn: 0.0338966	total: 37.6s	remaining: 24.1s
609:	learn: 0.0338629	total: 37.7s	remaining: 24.1s
610:	learn: 0.0337969	total: 37.7s	remaining: 24s
611:	learn: 0.0337811	total: 37.8s	remaining: 24s
612:	learn: 0.0337649	total: 37.9s	remaining: 23.9s
613:	learn: 0.0337231	total: 37.9s	remaining: 23.8s
614:	learn: 0.0336467	total: 38s	remaining: 23.8s
615:	learn: 0.0335667	total: 38.1s	remaining: 23.7s
616:	learn: 0.0335172	total: 38.1s	remaining: 23.7s
617:	learn: 0.0334334	total: 38.2s	remaining: 23.6s
618:	learn: 0.0333991	total: 38.3s	remaining: 23.6s
619:	learn: 0.0333718	total: 38.3s	remaining: 23.5s
620:	learn: 0.0333651	total: 38.4s	remaining: 23.4s
621:	learn: 0.0333056	total: 38.5s	remaining: 23.4s
622:	learn: 0.0332832	total: 38.5s	remaining: 23.3s
623:	learn: 0.0332365	total: 38.6s	remaining: 23.2s
624:	learn: 0.0332216	total: 38.6s	remaining: 23.2s
625:	learn: 0.0331770	total: 38.7s	remaining: 23.1s
626:	learn: 0.0331206	total: 38.8s	remaining: 23.1s
627:	learn: 0.0330807	total: 38.8s	remaining: 23s
628:	learn: 0.0330217	total: 38.9s	remaining: 22.9s
629:	learn: 0.0330024	total: 39s	remaining: 22.9s
630:	learn: 0.0329560	total: 39s	remaining: 22.8s
631:	learn: 0.0329270	total: 39.1s	remaining: 22.8s
632:	learn: 0.0329146	total: 39.1s	remaining: 22.7s
633:	learn: 0.0328830	total: 39.2s	remaining: 22.6s
634:	learn: 0.0328731	total: 39.2s	remaining: 22.6s
635:	learn: 0.0328448	total: 39.3s	remaining: 22.5s
636:	learn: 0.0328330	total: 39.4s	remaining: 22.4s
637:	learn: 0.0327829	total: 39.4s	remaining: 22.4s
638:	learn: 0.0327137	total: 39.5s	remaining: 22.3s
639:	learn: 0.0326759	total: 39.6s	remaining: 22.3s
640:	learn: 0.0326385	total: 39.6s	remaining: 22.2s
641:	learn: 0.0326214	total: 39.7s	remaining: 22.1s
642:	learn: 0.0325855	total: 39.7s	remaining: 22.1s
643:	learn: 0.0325262	total: 39.8s	remaining: 22s
644:	learn: 0.0325079	total: 39.9s	remaining: 21.9s
645:	learn: 0.0324724	total: 39.9s	remaining: 21.9s
646:	learn: 0.0324013	total: 40s	remaining: 21.8s
647:	learn: 0.0323625	total: 40.1s	remaining: 21.8s
648:	learn: 0.0323401	total: 40.1s	remaining: 21.7s
649:	learn: 0.0322816	total: 40.2s	remaining: 21.6s
650:	learn: 0.0322595	total: 40.2s	remaining: 21.6s
651:	learn: 0.0322199	total: 40.3s	remaining: 21.5s
652:	learn: 0.0321978	total: 40.4s	remaining: 21.4s
653:	learn: 0.0321265	total: 40.4s	remaining: 21.4s
654:	learn: 0.0321004	total: 40.5s	remaining: 21.3s
655:	learn: 0.0320724	total: 40.5s	remaining: 21.3s
656:	learn: 0.0320434	total: 40.6s	remaining: 21.2s
657:	learn: 0.0319738	total: 40.7s	remaining: 21.1s
658:	learn: 0.0319440	total: 40.7s	remaining: 21.1s
659:	learn: 0.0319075	total: 40.8s	remaining: 21s
660:	learn: 0.0318366	total: 40.9s	remaining: 21s
661:	learn: 0.0317806	total: 40.9s	remaining: 20.9s
662:	learn: 0.0317714	total: 41s	remaining: 20.8s
663:	learn: 0.0317208	total: 41.1s	remaining: 20.8s
664:	learn: 0.0317059	total: 41.1s	remaining: 20.7s
665:	learn: 0.0316622	total: 41.2s	remaining: 20.7s
666:	learn: 0.0316303	total: 41.3s	remaining: 20.6s
667:	learn: 0.0315832	total: 41.3s	remaining: 20.5s
668:	learn: 0.0315564	total: 41.4s	remaining: 20.5s
669:	learn: 0.0315369	total: 41.5s	remaining: 20.4s
670:	learn: 0.0315281	total: 41.5s	remaining: 20.4s
671:	learn: 0.0314797	total: 41.6s	remaining: 20.3s
672:	learn: 0.0314402	total: 41.7s	remaining: 20.2s
673:	learn: 0.0314196	total: 41.7s	remaining: 20.2s
674:	learn: 0.0313945	total: 41.8s	remaining: 20.1s
675:	learn: 0.0313512	total: 41.8s	remaining: 20.1s
676:	learn: 0.0313047	total: 41.9s	remaining: 20s
677:	learn: 0.0312564	total: 42s	remaining: 19.9s
678:	learn: 0.0312169	total: 42s	remaining: 19.9s
679:	learn: 0.0312114	total: 42.1s	remaining: 19.8s
680:	learn: 0.0311812	total: 42.2s	remaining: 19.7s
681:	learn: 0.0311336	total: 42.2s	remaining: 19.7s
682:	learn: 0.0311085	total: 42.3s	remaining: 19.6s
683:	learn: 0.0310382	total: 42.4s	remaining: 19.6s
684:	learn: 0.0310094	total: 42.4s	remaining: 19.5s
685:	learn: 0.0310025	total: 42.5s	remaining: 19.4s
686:	learn: 0.0309718	total: 42.5s	remaining: 19.4s
687:	learn: 0.0309190	total: 42.6s	remaining: 19.3s
688:	learn: 0.0308671	total: 42.7s	remaining: 19.3s
689:	learn: 0.0308490	total: 42.7s	remaining: 19.2s
690:	learn: 0.0307850	total: 42.8s	remaining: 19.1s
691:	learn: 0.0307686	total: 42.9s	remaining: 19.1s
692:	learn: 0.0307203	total: 43s	remaining: 19s
693:	learn: 0.0306676	total: 43s	remaining: 19s
694:	learn: 0.0306212	total: 43.1s	remaining: 18.9s
695:	learn: 0.0305946	total: 43.2s	remaining: 18.8s
696:	learn: 0.0305839	total: 43.2s	remaining: 18.8s
697:	learn: 0.0305652	total: 43.3s	remaining: 18.7s
698:	learn: 0.0305025	total: 43.3s	remaining: 18.7s
699:	learn: 0.0304431	total: 43.4s	remaining: 18.6s
700:	learn: 0.0304256	total: 43.5s	remaining: 18.5s
701:	learn: 0.0304193	total: 43.5s	remaining: 18.5s
702:	learn: 0.0304109	total: 43.6s	remaining: 18.4s
703:	learn: 0.0304015	total: 43.6s	remaining: 18.4s
704:	learn: 0.0303679	total: 43.7s	remaining: 18.3s
705:	learn: 0.0303417	total: 43.8s	remaining: 18.2s
706:	learn: 0.0303065	total: 43.8s	remaining: 18.2s
707:	learn: 0.0302678	total: 43.9s	remaining: 18.1s
708:	learn: 0.0302366	total: 44s	remaining: 18s
709:	learn: 0.0301983	total: 44s	remaining: 18s
710:	learn: 0.0301979	total: 44.1s	remaining: 17.9s
711:	learn: 0.0301721	total: 44.1s	remaining: 17.8s
712:	learn: 0.0301259	total: 44.2s	remaining: 17.8s
713:	learn: 0.0301065	total: 44.2s	remaining: 17.7s
714:	learn: 0.0300698	total: 44.3s	remaining: 17.7s
715:	learn: 0.0300534	total: 44.4s	remaining: 17.6s
716:	learn: 0.0299924	total: 44.4s	remaining: 17.5s
717:	learn: 0.0299577	total: 44.5s	remaining: 17.5s
718:	learn: 0.0299132	total: 44.5s	remaining: 17.4s
719:	learn: 0.0298532	total: 44.6s	remaining: 17.3s
720:	learn: 0.0298028	total: 44.7s	remaining: 17.3s
721:	learn: 0.0297676	total: 44.7s	remaining: 17.2s
722:	learn: 0.0297596	total: 44.8s	remaining: 17.2s
723:	learn: 0.0297068	total: 44.9s	remaining: 17.1s
724:	learn: 0.0296462	total: 44.9s	remaining: 17s
725:	learn: 0.0296099	total: 45s	remaining: 17s
726:	learn: 0.0295916	total: 45.1s	remaining: 16.9s
727:	learn: 0.0295759	total: 45.1s	remaining: 16.9s
728:	learn: 0.0295482	total: 45.2s	remaining: 16.8s
729:	learn: 0.0295400	total: 45.2s	remaining: 16.7s
730:	learn: 0.0295071	total: 45.3s	remaining: 16.7s
731:	learn: 0.0294656	total: 45.4s	remaining: 16.6s
732:	learn: 0.0294515	total: 45.4s	remaining: 16.6s
733:	learn: 0.0294376	total: 45.5s	remaining: 16.5s
734:	learn: 0.0294041	total: 45.5s	remaining: 16.4s
735:	learn: 0.0293613	total: 45.6s	remaining: 16.4s
736:	learn: 0.0293239	total: 45.7s	remaining: 16.3s
737:	learn: 0.0292787	total: 45.7s	remaining: 16.2s
738:	learn: 0.0292594	total: 45.8s	remaining: 16.2s
739:	learn: 0.0291984	total: 45.9s	remaining: 16.1s
740:	learn: 0.0291573	total: 45.9s	remaining: 16s
741:	learn: 0.0291140	total: 46s	remaining: 16s
742:	learn: 0.0290679	total: 46.1s	remaining: 15.9s
743:	learn: 0.0290016	total: 46.1s	remaining: 15.9s
744:	learn: 0.0289769	total: 46.2s	remaining: 15.8s
745:	learn: 0.0289434	total: 46.3s	remaining: 15.7s
746:	learn: 0.0289054	total: 46.3s	remaining: 15.7s
747:	learn: 0.0288845	total: 46.4s	remaining: 15.6s
748:	learn: 0.0288642	total: 46.4s	remaining: 15.6s
749:	learn: 0.0288337	total: 46.5s	remaining: 15.5s
750:	learn: 0.0287824	total: 46.6s	remaining: 15.4s
751:	learn: 0.0287535	total: 46.6s	remaining: 15.4s
752:	learn: 0.0287374	total: 46.7s	remaining: 15.3s
753:	learn: 0.0286974	total: 46.8s	remaining: 15.3s
754:	learn: 0.0286846	total: 46.8s	remaining: 15.2s
755:	learn: 0.0286368	total: 46.9s	remaining: 15.1s
756:	learn: 0.0285865	total: 47s	remaining: 15.1s
757:	learn: 0.0285754	total: 47s	remaining: 15s
758:	learn: 0.0285622	total: 47.1s	remaining: 14.9s
759:	learn: 0.0285329	total: 47.1s	remaining: 14.9s
760:	learn: 0.0285141	total: 47.2s	remaining: 14.8s
761:	learn: 0.0284996	total: 47.2s	remaining: 14.8s
762:	learn: 0.0284502	total: 47.3s	remaining: 14.7s
763:	learn: 0.0284106	total: 47.4s	remaining: 14.6s
764:	learn: 0.0283902	total: 47.4s	remaining: 14.6s
765:	learn: 0.0283380	total: 47.5s	remaining: 14.5s
766:	learn: 0.0283265	total: 47.6s	remaining: 14.4s
767:	learn: 0.0283149	total: 47.6s	remaining: 14.4s
768:	learn: 0.0282978	total: 47.7s	remaining: 14.3s
769:	learn: 0.0282459	total: 47.7s	remaining: 14.3s
770:	learn: 0.0282244	total: 47.8s	remaining: 14.2s
771:	learn: 0.0282019	total: 47.8s	remaining: 14.1s
772:	learn: 0.0281878	total: 47.9s	remaining: 14.1s
773:	learn: 0.0281785	total: 48s	remaining: 14s
774:	learn: 0.0281485	total: 48s	remaining: 13.9s
775:	learn: 0.0281361	total: 48.1s	remaining: 13.9s
776:	learn: 0.0280804	total: 48.2s	remaining: 13.8s
777:	learn: 0.0280653	total: 48.2s	remaining: 13.8s
778:	learn: 0.0280266	total: 48.3s	remaining: 13.7s
779:	learn: 0.0280097	total: 48.4s	remaining: 13.6s
780:	learn: 0.0279575	total: 48.4s	remaining: 13.6s
781:	learn: 0.0279382	total: 48.5s	remaining: 13.5s
782:	learn: 0.0279187	total: 48.5s	remaining: 13.4s
783:	learn: 0.0278523	total: 48.6s	remaining: 13.4s
784:	learn: 0.0278220	total: 48.7s	remaining: 13.3s
785:	learn: 0.0277730	total: 48.7s	remaining: 13.3s
786:	learn: 0.0277197	total: 48.8s	remaining: 13.2s
787:	learn: 0.0276785	total: 48.9s	remaining: 13.2s
788:	learn: 0.0276663	total: 49s	remaining: 13.1s
789:	learn: 0.0276623	total: 49s	remaining: 13s
790:	learn: 0.0276361	total: 49.1s	remaining: 13s
791:	learn: 0.0276185	total: 49.1s	remaining: 12.9s
792:	learn: 0.0275867	total: 49.2s	remaining: 12.8s
793:	learn: 0.0275703	total: 49.3s	remaining: 12.8s
794:	learn: 0.0275314	total: 49.3s	remaining: 12.7s
795:	learn: 0.0274973	total: 49.4s	remaining: 12.7s
796:	learn: 0.0274376	total: 49.5s	remaining: 12.6s
797:	learn: 0.0274157	total: 49.5s	remaining: 12.5s
798:	learn: 0.0273583	total: 49.6s	remaining: 12.5s
799:	learn: 0.0273039	total: 49.7s	remaining: 12.4s
800:	learn: 0.0272931	total: 49.7s	remaining: 12.4s
801:	learn: 0.0272818	total: 49.8s	remaining: 12.3s
802:	learn: 0.0272791	total: 49.8s	remaining: 12.2s
803:	learn: 0.0272536	total: 49.9s	remaining: 12.2s
804:	learn: 0.0272406	total: 50s	remaining: 12.1s
805:	learn: 0.0272022	total: 50s	remaining: 12s
806:	learn: 0.0271981	total: 50.1s	remaining: 12s
807:	learn: 0.0271632	total: 50.1s	remaining: 11.9s
808:	learn: 0.0271522	total: 50.2s	remaining: 11.9s
809:	learn: 0.0271488	total: 50.3s	remaining: 11.8s
810:	learn: 0.0271091	total: 50.3s	remaining: 11.7s
811:	learn: 0.0270720	total: 50.4s	remaining: 11.7s
812:	learn: 0.0270631	total: 50.5s	remaining: 11.6s
813:	learn: 0.0270249	total: 50.5s	remaining: 11.5s
814:	learn: 0.0269944	total: 50.6s	remaining: 11.5s
815:	learn: 0.0269777	total: 50.7s	remaining: 11.4s
816:	learn: 0.0269748	total: 50.7s	remaining: 11.4s
817:	learn: 0.0269422	total: 50.8s	remaining: 11.3s
818:	learn: 0.0268909	total: 50.9s	remaining: 11.2s
819:	learn: 0.0268822	total: 50.9s	remaining: 11.2s
820:	learn: 0.0268558	total: 51s	remaining: 11.1s
821:	learn: 0.0268089	total: 51s	remaining: 11.1s
822:	learn: 0.0267828	total: 51.1s	remaining: 11s
823:	learn: 0.0267513	total: 51.1s	remaining: 10.9s
824:	learn: 0.0267300	total: 51.2s	remaining: 10.9s
825:	learn: 0.0267039	total: 51.3s	remaining: 10.8s
826:	learn: 0.0266659	total: 51.4s	remaining: 10.7s
827:	learn: 0.0266303	total: 51.4s	remaining: 10.7s
828:	learn: 0.0265842	total: 51.5s	remaining: 10.6s
829:	learn: 0.0265443	total: 51.6s	remaining: 10.6s
830:	learn: 0.0265274	total: 51.6s	remaining: 10.5s
831:	learn: 0.0264997	total: 51.7s	remaining: 10.4s
832:	learn: 0.0264666	total: 51.8s	remaining: 10.4s
833:	learn: 0.0264292	total: 51.8s	remaining: 10.3s
834:	learn: 0.0264094	total: 51.9s	remaining: 10.3s
835:	learn: 0.0264091	total: 52s	remaining: 10.2s
836:	learn: 0.0263978	total: 52s	remaining: 10.1s
837:	learn: 0.0263592	total: 52.1s	remaining: 10.1s
838:	learn: 0.0263232	total: 52.2s	remaining: 10s
839:	learn: 0.0263175	total: 52.2s	remaining: 9.95s
840:	learn: 0.0263172	total: 52.3s	remaining: 9.88s
841:	learn: 0.0262953	total: 52.3s	remaining: 9.82s
842:	learn: 0.0262714	total: 52.4s	remaining: 9.76s
843:	learn: 0.0262189	total: 52.5s	remaining: 9.7s
844:	learn: 0.0261649	total: 52.5s	remaining: 9.64s
845:	learn: 0.0261253	total: 52.6s	remaining: 9.58s
846:	learn: 0.0261151	total: 52.7s	remaining: 9.52s
847:	learn: 0.0261008	total: 52.7s	remaining: 9.45s
848:	learn: 0.0260886	total: 52.8s	remaining: 9.39s
849:	learn: 0.0260510	total: 52.9s	remaining: 9.33s
850:	learn: 0.0260143	total: 52.9s	remaining: 9.27s
851:	learn: 0.0259823	total: 53s	remaining: 9.21s
852:	learn: 0.0259760	total: 53.1s	remaining: 9.14s
853:	learn: 0.0259504	total: 53.1s	remaining: 9.08s
854:	learn: 0.0259136	total: 53.2s	remaining: 9.02s
855:	learn: 0.0258907	total: 53.2s	remaining: 8.96s
856:	learn: 0.0258603	total: 53.3s	remaining: 8.89s
857:	learn: 0.0258334	total: 53.4s	remaining: 8.83s
858:	learn: 0.0258021	total: 53.4s	remaining: 8.77s
859:	learn: 0.0257630	total: 53.5s	remaining: 8.71s
860:	learn: 0.0257463	total: 53.6s	remaining: 8.65s
861:	learn: 0.0257360	total: 53.6s	remaining: 8.59s
862:	learn: 0.0257171	total: 53.7s	remaining: 8.52s
863:	learn: 0.0256916	total: 53.8s	remaining: 8.46s
864:	learn: 0.0256774	total: 53.8s	remaining: 8.4s
865:	learn: 0.0256721	total: 53.9s	remaining: 8.34s
866:	learn: 0.0256424	total: 53.9s	remaining: 8.28s
867:	learn: 0.0256359	total: 54s	remaining: 8.21s
868:	learn: 0.0256022	total: 54.1s	remaining: 8.15s
869:	learn: 0.0255827	total: 54.1s	remaining: 8.09s
870:	learn: 0.0255790	total: 54.2s	remaining: 8.02s
871:	learn: 0.0255570	total: 54.2s	remaining: 7.96s
872:	learn: 0.0255168	total: 54.3s	remaining: 7.9s
873:	learn: 0.0254985	total: 54.4s	remaining: 7.84s
874:	learn: 0.0254664	total: 54.4s	remaining: 7.77s
875:	learn: 0.0254282	total: 54.5s	remaining: 7.71s
876:	learn: 0.0253895	total: 54.6s	remaining: 7.65s
877:	learn: 0.0253680	total: 54.6s	remaining: 7.59s
878:	learn: 0.0253354	total: 54.7s	remaining: 7.53s
879:	learn: 0.0252976	total: 54.8s	remaining: 7.47s
880:	learn: 0.0252874	total: 54.8s	remaining: 7.4s
881:	learn: 0.0252683	total: 54.9s	remaining: 7.34s
882:	learn: 0.0252182	total: 54.9s	remaining: 7.28s
883:	learn: 0.0251953	total: 55s	remaining: 7.22s
884:	learn: 0.0251812	total: 55.1s	remaining: 7.15s
885:	learn: 0.0251712	total: 55.1s	remaining: 7.09s
886:	learn: 0.0251394	total: 55.2s	remaining: 7.03s
887:	learn: 0.0251233	total: 55.2s	remaining: 6.97s
888:	learn: 0.0250945	total: 55.3s	remaining: 6.91s
889:	learn: 0.0250874	total: 55.4s	remaining: 6.84s
890:	learn: 0.0250691	total: 55.4s	remaining: 6.78s
891:	learn: 0.0250397	total: 55.5s	remaining: 6.72s
892:	learn: 0.0250075	total: 55.6s	remaining: 6.66s
893:	learn: 0.0249770	total: 55.7s	remaining: 6.6s
894:	learn: 0.0249736	total: 55.7s	remaining: 6.54s
895:	learn: 0.0249497	total: 55.8s	remaining: 6.47s
896:	learn: 0.0249396	total: 55.8s	remaining: 6.41s
897:	learn: 0.0249244	total: 55.9s	remaining: 6.35s
898:	learn: 0.0248864	total: 55.9s	remaining: 6.29s
899:	learn: 0.0248838	total: 56s	remaining: 6.22s
900:	learn: 0.0248757	total: 56.1s	remaining: 6.16s
901:	learn: 0.0248665	total: 56.1s	remaining: 6.1s
902:	learn: 0.0248607	total: 56.2s	remaining: 6.03s
903:	learn: 0.0248376	total: 56.2s	remaining: 5.97s
904:	learn: 0.0248156	total: 56.3s	remaining: 5.91s
905:	learn: 0.0247865	total: 56.4s	remaining: 5.85s
906:	learn: 0.0247795	total: 56.4s	remaining: 5.79s
907:	learn: 0.0247766	total: 56.5s	remaining: 5.72s
908:	learn: 0.0247566	total: 56.5s	remaining: 5.66s
909:	learn: 0.0247485	total: 56.6s	remaining: 5.6s
910:	learn: 0.0247247	total: 56.7s	remaining: 5.54s
911:	learn: 0.0246921	total: 56.7s	remaining: 5.47s
912:	learn: 0.0246854	total: 56.8s	remaining: 5.41s
913:	learn: 0.0246599	total: 56.9s	remaining: 5.35s
914:	learn: 0.0246405	total: 56.9s	remaining: 5.29s
915:	learn: 0.0246178	total: 57s	remaining: 5.22s
916:	learn: 0.0246060	total: 57s	remaining: 5.16s
917:	learn: 0.0245890	total: 57.1s	remaining: 5.1s
918:	learn: 0.0245513	total: 57.2s	remaining: 5.04s
919:	learn: 0.0245288	total: 57.2s	remaining: 4.98s
920:	learn: 0.0245180	total: 57.3s	remaining: 4.91s
921:	learn: 0.0244847	total: 57.3s	remaining: 4.85s
922:	learn: 0.0244612	total: 57.4s	remaining: 4.79s
923:	learn: 0.0244212	total: 57.5s	remaining: 4.73s
924:	learn: 0.0244065	total: 57.6s	remaining: 4.67s
925:	learn: 0.0243692	total: 57.6s	remaining: 4.61s
926:	learn: 0.0243552	total: 57.7s	remaining: 4.54s
927:	learn: 0.0243235	total: 57.8s	remaining: 4.48s
928:	learn: 0.0243153	total: 57.8s	remaining: 4.42s
929:	learn: 0.0243051	total: 57.9s	remaining: 4.36s
930:	learn: 0.0242839	total: 57.9s	remaining: 4.29s
931:	learn: 0.0242487	total: 58s	remaining: 4.23s
932:	learn: 0.0242230	total: 58.1s	remaining: 4.17s
933:	learn: 0.0242016	total: 58.1s	remaining: 4.11s
934:	learn: 0.0241883	total: 58.2s	remaining: 4.04s
935:	learn: 0.0241847	total: 58.2s	remaining: 3.98s
936:	learn: 0.0241492	total: 58.3s	remaining: 3.92s
937:	learn: 0.0241414	total: 58.4s	remaining: 3.86s
938:	learn: 0.0241287	total: 58.4s	remaining: 3.79s
939:	learn: 0.0241258	total: 58.5s	remaining: 3.73s
940:	learn: 0.0241028	total: 58.5s	remaining: 3.67s
941:	learn: 0.0240714	total: 58.6s	remaining: 3.61s
942:	learn: 0.0240214	total: 58.7s	remaining: 3.55s
943:	learn: 0.0240031	total: 58.7s	remaining: 3.48s
944:	learn: 0.0239966	total: 58.8s	remaining: 3.42s
945:	learn: 0.0239579	total: 58.9s	remaining: 3.36s
946:	learn: 0.0239263	total: 58.9s	remaining: 3.3s
947:	learn: 0.0239037	total: 59s	remaining: 3.24s
948:	learn: 0.0238789	total: 59.1s	remaining: 3.17s
949:	learn: 0.0238723	total: 59.1s	remaining: 3.11s
950:	learn: 0.0238647	total: 59.2s	remaining: 3.05s
951:	learn: 0.0238469	total: 59.2s	remaining: 2.99s
952:	learn: 0.0238286	total: 59.3s	remaining: 2.92s
953:	learn: 0.0237938	total: 59.4s	remaining: 2.86s
954:	learn: 0.0237779	total: 59.4s	remaining: 2.8s
955:	learn: 0.0237464	total: 59.5s	remaining: 2.74s
956:	learn: 0.0236934	total: 59.6s	remaining: 2.68s
957:	learn: 0.0236778	total: 59.6s	remaining: 2.61s
958:	learn: 0.0236700	total: 59.7s	remaining: 2.55s
959:	learn: 0.0236632	total: 59.7s	remaining: 2.49s
960:	learn: 0.0236557	total: 59.8s	remaining: 2.43s
961:	learn: 0.0236340	total: 59.9s	remaining: 2.36s
962:	learn: 0.0235898	total: 59.9s	remaining: 2.3s
963:	learn: 0.0235688	total: 60s	remaining: 2.24s
964:	learn: 0.0235601	total: 1m	remaining: 2.18s
965:	learn: 0.0235372	total: 1m	remaining: 2.12s
966:	learn: 0.0235292	total: 1m	remaining: 2.05s
967:	learn: 0.0234970	total: 1m	remaining: 1.99s
968:	learn: 0.0234880	total: 1m	remaining: 1.93s
969:	learn: 0.0234716	total: 1m	remaining: 1.87s
970:	learn: 0.0234386	total: 1m	remaining: 1.8s
971:	learn: 0.0233936	total: 1m	remaining: 1.74s
972:	learn: 0.0233851	total: 1m	remaining: 1.68s
973:	learn: 0.0233714	total: 1m	remaining: 1.62s
974:	learn: 0.0233535	total: 1m	remaining: 1.56s
975:	learn: 0.0233498	total: 1m	remaining: 1.49s
976:	learn: 0.0233474	total: 1m	remaining: 1.43s
977:	learn: 0.0233222	total: 1m	remaining: 1.37s
978:	learn: 0.0233149	total: 1m	remaining: 1.31s
979:	learn: 0.0233076	total: 1m	remaining: 1.24s
980:	learn: 0.0233006	total: 1m 1s	remaining: 1.18s
981:	learn: 0.0232944	total: 1m 1s	remaining: 1.12s
982:	learn: 0.0232648	total: 1m 1s	remaining: 1.06s
983:	learn: 0.0232241	total: 1m 1s	remaining: 996ms
984:	learn: 0.0232073	total: 1m 1s	remaining: 933ms
985:	learn: 0.0232031	total: 1m 1s	remaining: 871ms
986:	learn: 0.0231913	total: 1m 1s	remaining: 809ms
987:	learn: 0.0231879	total: 1m 1s	remaining: 747ms
988:	learn: 0.0231739	total: 1m 1s	remaining: 684ms
989:	learn: 0.0231605	total: 1m 1s	remaining: 622ms
990:	learn: 0.0231531	total: 1m 1s	remaining: 560ms
991:	learn: 0.0231486	total: 1m 1s	remaining: 498ms
992:	learn: 0.0231401	total: 1m 1s	remaining: 435ms
993:	learn: 0.0231210	total: 1m 1s	remaining: 373ms
994:	learn: 0.0231139	total: 1m 1s	remaining: 311ms
995:	learn: 0.0231036	total: 1m 1s	remaining: 249ms
996:	learn: 0.0230795	total: 1m 2s	remaining: 187ms
997:	learn: 0.0230629	total: 1m 2s	remaining: 124ms
998:	learn: 0.0230418	total: 1m 2s	remaining: 62.2ms
999:	learn: 0.0229833	total: 1m 2s	remaining: 0us
Learning rate set to 0.046653
0:	learn: 0.5983298	total: 76.2ms	remaining: 1m 16s
1:	learn: 0.5177007	total: 130ms	remaining: 1m 4s
2:	learn: 0.4493555	total: 193ms	remaining: 1m 4s
3:	learn: 0.3914267	total: 259ms	remaining: 1m 4s
4:	learn: 0.3426861	total: 328ms	remaining: 1m 5s
5:	learn: 0.3016517	total: 399ms	remaining: 1m 6s
6:	learn: 0.2666672	total: 469ms	remaining: 1m 6s
7:	learn: 0.2367279	total: 547ms	remaining: 1m 7s
8:	learn: 0.2124253	total: 619ms	remaining: 1m 8s
9:	learn: 0.1919390	total: 685ms	remaining: 1m 7s
10:	learn: 0.1744700	total: 748ms	remaining: 1m 7s
11:	learn: 0.1595808	total: 815ms	remaining: 1m 7s
12:	learn: 0.1470345	total: 878ms	remaining: 1m 6s
13:	learn: 0.1362302	total: 939ms	remaining: 1m 6s
14:	learn: 0.1270020	total: 1.01s	remaining: 1m 6s
15:	learn: 0.1191133	total: 1.06s	remaining: 1m 5s
16:	learn: 0.1122895	total: 1.13s	remaining: 1m 5s
17:	learn: 0.1064445	total: 1.2s	remaining: 1m 5s
18:	learn: 0.1013625	total: 1.27s	remaining: 1m 5s
19:	learn: 0.0965266	total: 1.34s	remaining: 1m 5s
20:	learn: 0.0927326	total: 1.41s	remaining: 1m 5s
21:	learn: 0.0894648	total: 1.48s	remaining: 1m 5s
22:	learn: 0.0865497	total: 1.56s	remaining: 1m 6s
23:	learn: 0.0840184	total: 1.62s	remaining: 1m 6s
24:	learn: 0.0818206	total: 1.7s	remaining: 1m 6s
25:	learn: 0.0793357	total: 1.77s	remaining: 1m 6s
26:	learn: 0.0776752	total: 1.85s	remaining: 1m 6s
27:	learn: 0.0756968	total: 1.91s	remaining: 1m 6s
28:	learn: 0.0744500	total: 1.98s	remaining: 1m 6s
29:	learn: 0.0733267	total: 2.05s	remaining: 1m 6s
30:	learn: 0.0722482	total: 2.12s	remaining: 1m 6s
31:	learn: 0.0704905	total: 2.2s	remaining: 1m 6s
32:	learn: 0.0695772	total: 2.27s	remaining: 1m 6s
33:	learn: 0.0689137	total: 2.33s	remaining: 1m 6s
34:	learn: 0.0682844	total: 2.41s	remaining: 1m 6s
35:	learn: 0.0677513	total: 2.48s	remaining: 1m 6s
36:	learn: 0.0672842	total: 2.54s	remaining: 1m 6s
37:	learn: 0.0661217	total: 2.63s	remaining: 1m 6s
38:	learn: 0.0655212	total: 2.7s	remaining: 1m 6s
39:	learn: 0.0651658	total: 2.77s	remaining: 1m 6s
40:	learn: 0.0647483	total: 2.85s	remaining: 1m 6s
41:	learn: 0.0639467	total: 2.92s	remaining: 1m 6s
42:	learn: 0.0632836	total: 2.99s	remaining: 1m 6s
43:	learn: 0.0626933	total: 3.06s	remaining: 1m 6s
44:	learn: 0.0621558	total: 3.13s	remaining: 1m 6s
45:	learn: 0.0619122	total: 3.21s	remaining: 1m 6s
46:	learn: 0.0617521	total: 3.27s	remaining: 1m 6s
47:	learn: 0.0615119	total: 3.35s	remaining: 1m 6s
48:	learn: 0.0613842	total: 3.41s	remaining: 1m 6s
49:	learn: 0.0611219	total: 3.48s	remaining: 1m 6s
50:	learn: 0.0607805	total: 3.55s	remaining: 1m 6s
51:	learn: 0.0604068	total: 3.63s	remaining: 1m 6s
52:	learn: 0.0603351	total: 3.69s	remaining: 1m 5s
53:	learn: 0.0602607	total: 3.74s	remaining: 1m 5s
54:	learn: 0.0601285	total: 3.81s	remaining: 1m 5s
55:	learn: 0.0598221	total: 3.9s	remaining: 1m 5s
56:	learn: 0.0597258	total: 3.96s	remaining: 1m 5s
57:	learn: 0.0596059	total: 4.03s	remaining: 1m 5s
58:	learn: 0.0593626	total: 4.11s	remaining: 1m 5s
59:	learn: 0.0590590	total: 4.19s	remaining: 1m 5s
60:	learn: 0.0590072	total: 4.24s	remaining: 1m 5s
61:	learn: 0.0587627	total: 4.32s	remaining: 1m 5s
62:	learn: 0.0585745	total: 4.39s	remaining: 1m 5s
63:	learn: 0.0583912	total: 4.46s	remaining: 1m 5s
64:	learn: 0.0582611	total: 4.53s	remaining: 1m 5s
65:	learn: 0.0581861	total: 4.59s	remaining: 1m 4s
66:	learn: 0.0581446	total: 4.65s	remaining: 1m 4s
67:	learn: 0.0580798	total: 4.71s	remaining: 1m 4s
68:	learn: 0.0580072	total: 4.76s	remaining: 1m 4s
69:	learn: 0.0578794	total: 4.84s	remaining: 1m 4s
70:	learn: 0.0577810	total: 4.9s	remaining: 1m 4s
71:	learn: 0.0576998	total: 4.97s	remaining: 1m 4s
72:	learn: 0.0576693	total: 5.03s	remaining: 1m 3s
73:	learn: 0.0575327	total: 5.11s	remaining: 1m 3s
74:	learn: 0.0573811	total: 5.18s	remaining: 1m 3s
75:	learn: 0.0572244	total: 5.26s	remaining: 1m 3s
76:	learn: 0.0571500	total: 5.32s	remaining: 1m 3s
77:	learn: 0.0570294	total: 5.4s	remaining: 1m 3s
78:	learn: 0.0568746	total: 5.47s	remaining: 1m 3s
79:	learn: 0.0568477	total: 5.52s	remaining: 1m 3s
80:	learn: 0.0567617	total: 5.59s	remaining: 1m 3s
81:	learn: 0.0567057	total: 5.67s	remaining: 1m 3s
82:	learn: 0.0565960	total: 5.74s	remaining: 1m 3s
83:	learn: 0.0565603	total: 5.81s	remaining: 1m 3s
84:	learn: 0.0564666	total: 5.87s	remaining: 1m 3s
85:	learn: 0.0564440	total: 5.92s	remaining: 1m 2s
86:	learn: 0.0564245	total: 5.98s	remaining: 1m 2s
87:	learn: 0.0563135	total: 6.05s	remaining: 1m 2s
88:	learn: 0.0562578	total: 6.12s	remaining: 1m 2s
89:	learn: 0.0561697	total: 6.18s	remaining: 1m 2s
90:	learn: 0.0561094	total: 6.24s	remaining: 1m 2s
91:	learn: 0.0560477	total: 6.29s	remaining: 1m 2s
92:	learn: 0.0559255	total: 6.37s	remaining: 1m 2s
93:	learn: 0.0559060	total: 6.43s	remaining: 1m 2s
94:	learn: 0.0558038	total: 6.5s	remaining: 1m 1s
95:	learn: 0.0557407	total: 6.56s	remaining: 1m 1s
96:	learn: 0.0556644	total: 6.65s	remaining: 1m 1s
97:	learn: 0.0556040	total: 6.71s	remaining: 1m 1s
98:	learn: 0.0555106	total: 6.78s	remaining: 1m 1s
99:	learn: 0.0553771	total: 6.86s	remaining: 1m 1s
100:	learn: 0.0552755	total: 6.93s	remaining: 1m 1s
101:	learn: 0.0551793	total: 7s	remaining: 1m 1s
102:	learn: 0.0551116	total: 7.07s	remaining: 1m 1s
103:	learn: 0.0550381	total: 7.15s	remaining: 1m 1s
104:	learn: 0.0550061	total: 7.21s	remaining: 1m 1s
105:	learn: 0.0549238	total: 7.27s	remaining: 1m 1s
106:	learn: 0.0548917	total: 7.32s	remaining: 1m 1s
107:	learn: 0.0548074	total: 7.39s	remaining: 1m 1s
108:	learn: 0.0547536	total: 7.46s	remaining: 1m
109:	learn: 0.0547148	total: 7.52s	remaining: 1m
110:	learn: 0.0546722	total: 7.57s	remaining: 1m
111:	learn: 0.0545884	total: 7.64s	remaining: 1m
112:	learn: 0.0545378	total: 7.7s	remaining: 1m
113:	learn: 0.0544749	total: 7.76s	remaining: 1m
114:	learn: 0.0544172	total: 7.82s	remaining: 1m
115:	learn: 0.0543947	total: 7.88s	remaining: 1m
116:	learn: 0.0543179	total: 7.94s	remaining: 59.9s
117:	learn: 0.0542672	total: 8s	remaining: 59.8s
118:	learn: 0.0542385	total: 8.06s	remaining: 59.7s
119:	learn: 0.0542034	total: 8.11s	remaining: 59.4s
120:	learn: 0.0541720	total: 8.15s	remaining: 59.2s
121:	learn: 0.0541331	total: 8.21s	remaining: 59.1s
122:	learn: 0.0540625	total: 8.27s	remaining: 58.9s
123:	learn: 0.0540047	total: 8.32s	remaining: 58.8s
124:	learn: 0.0539289	total: 8.38s	remaining: 58.6s
125:	learn: 0.0538968	total: 8.43s	remaining: 58.5s
126:	learn: 0.0538711	total: 8.48s	remaining: 58.3s
127:	learn: 0.0538164	total: 8.54s	remaining: 58.2s
128:	learn: 0.0537309	total: 8.6s	remaining: 58s
129:	learn: 0.0536592	total: 8.67s	remaining: 58s
130:	learn: 0.0536006	total: 8.72s	remaining: 57.8s
131:	learn: 0.0535542	total: 8.78s	remaining: 57.7s
132:	learn: 0.0534633	total: 8.85s	remaining: 57.7s
133:	learn: 0.0534114	total: 8.91s	remaining: 57.6s
134:	learn: 0.0533610	total: 8.98s	remaining: 57.5s
135:	learn: 0.0533346	total: 9.04s	remaining: 57.4s
136:	learn: 0.0533018	total: 9.1s	remaining: 57.3s
137:	learn: 0.0532789	total: 9.16s	remaining: 57.2s
138:	learn: 0.0532104	total: 9.22s	remaining: 57.1s
139:	learn: 0.0531824	total: 9.27s	remaining: 56.9s
140:	learn: 0.0531474	total: 9.32s	remaining: 56.8s
141:	learn: 0.0531006	total: 9.37s	remaining: 56.6s
142:	learn: 0.0530597	total: 9.42s	remaining: 56.4s
143:	learn: 0.0529942	total: 9.48s	remaining: 56.4s
144:	learn: 0.0529598	total: 9.54s	remaining: 56.3s
145:	learn: 0.0528812	total: 9.61s	remaining: 56.2s
146:	learn: 0.0528001	total: 9.68s	remaining: 56.1s
147:	learn: 0.0527410	total: 9.73s	remaining: 56s
148:	learn: 0.0527066	total: 9.79s	remaining: 55.9s
149:	learn: 0.0526765	total: 9.85s	remaining: 55.8s
150:	learn: 0.0525769	total: 9.95s	remaining: 56s
151:	learn: 0.0525462	total: 10s	remaining: 55.8s
152:	learn: 0.0525025	total: 10.1s	remaining: 55.8s
153:	learn: 0.0524877	total: 10.1s	remaining: 55.6s
154:	learn: 0.0523896	total: 10.2s	remaining: 55.5s
155:	learn: 0.0523171	total: 10.2s	remaining: 55.4s
156:	learn: 0.0521990	total: 10.3s	remaining: 55.4s
157:	learn: 0.0521661	total: 10.4s	remaining: 55.2s
158:	learn: 0.0521209	total: 10.4s	remaining: 55.2s
159:	learn: 0.0520812	total: 10.5s	remaining: 55.1s
160:	learn: 0.0520436	total: 10.5s	remaining: 55s
161:	learn: 0.0519711	total: 10.6s	remaining: 54.9s
162:	learn: 0.0519172	total: 10.7s	remaining: 54.8s
163:	learn: 0.0518763	total: 10.7s	remaining: 54.7s
164:	learn: 0.0518144	total: 10.8s	remaining: 54.6s
165:	learn: 0.0517953	total: 10.9s	remaining: 54.6s
166:	learn: 0.0517748	total: 10.9s	remaining: 54.5s
167:	learn: 0.0517269	total: 11s	remaining: 54.4s
168:	learn: 0.0516743	total: 11.1s	remaining: 54.4s
169:	learn: 0.0515658	total: 11.1s	remaining: 54.3s
170:	learn: 0.0514857	total: 11.2s	remaining: 54.3s
171:	learn: 0.0514397	total: 11.2s	remaining: 54.1s
172:	learn: 0.0513290	total: 11.3s	remaining: 54.1s
173:	learn: 0.0513125	total: 11.4s	remaining: 53.9s
174:	learn: 0.0512478	total: 11.4s	remaining: 53.8s
175:	learn: 0.0512112	total: 11.5s	remaining: 53.7s
176:	learn: 0.0511401	total: 11.5s	remaining: 53.6s
177:	learn: 0.0510883	total: 11.6s	remaining: 53.5s
178:	learn: 0.0510559	total: 11.7s	remaining: 53.5s
179:	learn: 0.0510124	total: 11.7s	remaining: 53.4s
180:	learn: 0.0509613	total: 11.8s	remaining: 53.3s
181:	learn: 0.0509056	total: 11.8s	remaining: 53.2s
182:	learn: 0.0508444	total: 11.9s	remaining: 53.1s
183:	learn: 0.0508093	total: 11.9s	remaining: 53s
184:	learn: 0.0507808	total: 12s	remaining: 52.9s
185:	learn: 0.0507420	total: 12.1s	remaining: 52.8s
186:	learn: 0.0506717	total: 12.1s	remaining: 52.7s
187:	learn: 0.0506075	total: 12.2s	remaining: 52.6s
188:	learn: 0.0505502	total: 12.2s	remaining: 52.5s
189:	learn: 0.0505287	total: 12.3s	remaining: 52.4s
190:	learn: 0.0505020	total: 12.3s	remaining: 52.3s
191:	learn: 0.0504605	total: 12.4s	remaining: 52.2s
192:	learn: 0.0504317	total: 12.5s	remaining: 52.1s
193:	learn: 0.0504105	total: 12.5s	remaining: 52s
194:	learn: 0.0503953	total: 12.6s	remaining: 51.9s
195:	learn: 0.0503352	total: 12.6s	remaining: 51.8s
196:	learn: 0.0502847	total: 12.7s	remaining: 51.7s
197:	learn: 0.0502318	total: 12.7s	remaining: 51.6s
198:	learn: 0.0501655	total: 12.8s	remaining: 51.6s
199:	learn: 0.0501392	total: 12.9s	remaining: 51.5s
200:	learn: 0.0500951	total: 12.9s	remaining: 51.4s
201:	learn: 0.0500120	total: 13s	remaining: 51.3s
202:	learn: 0.0499686	total: 13s	remaining: 51.2s
203:	learn: 0.0499131	total: 13.1s	remaining: 51.1s
204:	learn: 0.0498933	total: 13.2s	remaining: 51s
205:	learn: 0.0498590	total: 13.2s	remaining: 50.9s
206:	learn: 0.0497851	total: 13.3s	remaining: 50.9s
207:	learn: 0.0497353	total: 13.3s	remaining: 50.8s
208:	learn: 0.0496565	total: 13.4s	remaining: 50.7s
209:	learn: 0.0496182	total: 13.5s	remaining: 50.6s
210:	learn: 0.0495639	total: 13.5s	remaining: 50.5s
211:	learn: 0.0495412	total: 13.6s	remaining: 50.4s
212:	learn: 0.0494672	total: 13.6s	remaining: 50.3s
213:	learn: 0.0494052	total: 13.7s	remaining: 50.2s
214:	learn: 0.0493420	total: 13.8s	remaining: 50.2s
215:	learn: 0.0493106	total: 13.8s	remaining: 50.1s
216:	learn: 0.0492919	total: 13.9s	remaining: 50s
217:	learn: 0.0492702	total: 13.9s	remaining: 49.9s
218:	learn: 0.0492159	total: 14s	remaining: 49.8s
219:	learn: 0.0491340	total: 14s	remaining: 49.8s
220:	learn: 0.0490828	total: 14.1s	remaining: 49.7s
221:	learn: 0.0489824	total: 14.2s	remaining: 49.7s
222:	learn: 0.0489342	total: 14.2s	remaining: 49.6s
223:	learn: 0.0489207	total: 14.3s	remaining: 49.4s
224:	learn: 0.0488973	total: 14.3s	remaining: 49.3s
225:	learn: 0.0488258	total: 14.4s	remaining: 49.2s
226:	learn: 0.0487704	total: 14.4s	remaining: 49.2s
227:	learn: 0.0486873	total: 14.5s	remaining: 49.1s
228:	learn: 0.0486459	total: 14.6s	remaining: 49s
229:	learn: 0.0485869	total: 14.6s	remaining: 49s
230:	learn: 0.0485331	total: 14.7s	remaining: 48.9s
231:	learn: 0.0484645	total: 14.8s	remaining: 48.8s
232:	learn: 0.0484323	total: 14.8s	remaining: 48.8s
233:	learn: 0.0484097	total: 14.9s	remaining: 48.7s
234:	learn: 0.0483880	total: 14.9s	remaining: 48.6s
235:	learn: 0.0483434	total: 15s	remaining: 48.5s
236:	learn: 0.0483089	total: 15s	remaining: 48.4s
237:	learn: 0.0482867	total: 15.1s	remaining: 48.3s
238:	learn: 0.0482315	total: 15.1s	remaining: 48.2s
239:	learn: 0.0482185	total: 15.2s	remaining: 48.1s
240:	learn: 0.0481870	total: 15.3s	remaining: 48s
241:	learn: 0.0481412	total: 15.3s	remaining: 48s
242:	learn: 0.0481037	total: 15.4s	remaining: 47.9s
243:	learn: 0.0480403	total: 15.4s	remaining: 47.8s
244:	learn: 0.0479773	total: 15.5s	remaining: 47.8s
245:	learn: 0.0479470	total: 15.5s	remaining: 47.7s
246:	learn: 0.0478890	total: 15.6s	remaining: 47.6s
247:	learn: 0.0478519	total: 15.7s	remaining: 47.5s
248:	learn: 0.0478256	total: 15.7s	remaining: 47.4s
249:	learn: 0.0477883	total: 15.8s	remaining: 47.3s
250:	learn: 0.0477517	total: 15.8s	remaining: 47.3s
251:	learn: 0.0477218	total: 15.9s	remaining: 47.2s
252:	learn: 0.0476852	total: 16s	remaining: 47.1s
253:	learn: 0.0476528	total: 16s	remaining: 47s
254:	learn: 0.0476217	total: 16.1s	remaining: 46.9s
255:	learn: 0.0475600	total: 16.1s	remaining: 46.8s
256:	learn: 0.0475080	total: 16.2s	remaining: 46.8s
257:	learn: 0.0474284	total: 16.2s	remaining: 46.7s
258:	learn: 0.0473694	total: 16.3s	remaining: 46.6s
259:	learn: 0.0473212	total: 16.4s	remaining: 46.5s
260:	learn: 0.0472584	total: 16.4s	remaining: 46.5s
261:	learn: 0.0472360	total: 16.5s	remaining: 46.4s
262:	learn: 0.0472011	total: 16.5s	remaining: 46.3s
263:	learn: 0.0471717	total: 16.6s	remaining: 46.2s
264:	learn: 0.0471280	total: 16.7s	remaining: 46.2s
265:	learn: 0.0470421	total: 16.7s	remaining: 46.1s
266:	learn: 0.0470029	total: 16.8s	remaining: 46s
267:	learn: 0.0469308	total: 16.8s	remaining: 46s
268:	learn: 0.0468734	total: 16.9s	remaining: 45.9s
269:	learn: 0.0467763	total: 17s	remaining: 45.9s
270:	learn: 0.0467279	total: 17s	remaining: 45.8s
271:	learn: 0.0466863	total: 17.1s	remaining: 45.7s
272:	learn: 0.0466469	total: 17.1s	remaining: 45.6s
273:	learn: 0.0466045	total: 17.2s	remaining: 45.6s
274:	learn: 0.0465752	total: 17.2s	remaining: 45.5s
275:	learn: 0.0464883	total: 17.3s	remaining: 45.4s
276:	learn: 0.0464331	total: 17.4s	remaining: 45.3s
277:	learn: 0.0464232	total: 17.4s	remaining: 45.2s
278:	learn: 0.0463946	total: 17.5s	remaining: 45.2s
279:	learn: 0.0463504	total: 17.5s	remaining: 45.1s
280:	learn: 0.0462652	total: 17.6s	remaining: 45.1s
281:	learn: 0.0462393	total: 17.7s	remaining: 45s
282:	learn: 0.0461741	total: 17.7s	remaining: 44.9s
283:	learn: 0.0461544	total: 17.8s	remaining: 44.8s
284:	learn: 0.0460795	total: 17.9s	remaining: 44.8s
285:	learn: 0.0460660	total: 17.9s	remaining: 44.7s
286:	learn: 0.0459505	total: 18s	remaining: 44.6s
287:	learn: 0.0459018	total: 18s	remaining: 44.6s
288:	learn: 0.0458712	total: 18.1s	remaining: 44.5s
289:	learn: 0.0458150	total: 18.2s	remaining: 44.4s
290:	learn: 0.0457646	total: 18.2s	remaining: 44.4s
291:	learn: 0.0456992	total: 18.3s	remaining: 44.3s
292:	learn: 0.0456212	total: 18.3s	remaining: 44.3s
293:	learn: 0.0455951	total: 18.4s	remaining: 44.2s
294:	learn: 0.0455521	total: 18.5s	remaining: 44.1s
295:	learn: 0.0455353	total: 18.5s	remaining: 44s
296:	learn: 0.0455100	total: 18.6s	remaining: 43.9s
297:	learn: 0.0454440	total: 18.6s	remaining: 43.9s
298:	learn: 0.0454040	total: 18.7s	remaining: 43.8s
299:	learn: 0.0453544	total: 18.7s	remaining: 43.7s
300:	learn: 0.0453334	total: 18.8s	remaining: 43.6s
301:	learn: 0.0453247	total: 18.8s	remaining: 43.6s
302:	learn: 0.0452712	total: 18.9s	remaining: 43.5s
303:	learn: 0.0451999	total: 19s	remaining: 43.4s
304:	learn: 0.0451659	total: 19s	remaining: 43.4s
305:	learn: 0.0451108	total: 19.1s	remaining: 43.3s
306:	learn: 0.0450332	total: 19.2s	remaining: 43.2s
307:	learn: 0.0449974	total: 19.2s	remaining: 43.2s
308:	learn: 0.0449415	total: 19.3s	remaining: 43.1s
309:	learn: 0.0448742	total: 19.3s	remaining: 43s
310:	learn: 0.0448397	total: 19.4s	remaining: 43s
311:	learn: 0.0447680	total: 19.5s	remaining: 42.9s
312:	learn: 0.0447000	total: 19.5s	remaining: 42.9s
313:	learn: 0.0446840	total: 19.6s	remaining: 42.8s
314:	learn: 0.0446370	total: 19.6s	remaining: 42.7s
315:	learn: 0.0445629	total: 19.7s	remaining: 42.6s
316:	learn: 0.0445394	total: 19.8s	remaining: 42.6s
317:	learn: 0.0445247	total: 19.8s	remaining: 42.5s
318:	learn: 0.0444805	total: 19.9s	remaining: 42.4s
319:	learn: 0.0444359	total: 19.9s	remaining: 42.3s
320:	learn: 0.0443884	total: 20s	remaining: 42.3s
321:	learn: 0.0443718	total: 20s	remaining: 42.2s
322:	learn: 0.0443443	total: 20.1s	remaining: 42.1s
323:	learn: 0.0442962	total: 20.2s	remaining: 42.1s
324:	learn: 0.0442161	total: 20.2s	remaining: 42s
325:	learn: 0.0441673	total: 20.3s	remaining: 41.9s
326:	learn: 0.0441478	total: 20.3s	remaining: 41.8s
327:	learn: 0.0440863	total: 20.4s	remaining: 41.8s
328:	learn: 0.0440545	total: 20.5s	remaining: 41.7s
329:	learn: 0.0439727	total: 20.5s	remaining: 41.6s
330:	learn: 0.0438985	total: 20.6s	remaining: 41.6s
331:	learn: 0.0438853	total: 20.6s	remaining: 41.5s
332:	learn: 0.0438527	total: 20.7s	remaining: 41.4s
333:	learn: 0.0438383	total: 20.8s	remaining: 41.4s
334:	learn: 0.0437773	total: 20.8s	remaining: 41.3s
335:	learn: 0.0437431	total: 20.9s	remaining: 41.2s
336:	learn: 0.0436968	total: 20.9s	remaining: 41.2s
337:	learn: 0.0436869	total: 21s	remaining: 41.1s
338:	learn: 0.0436358	total: 21s	remaining: 41s
339:	learn: 0.0436179	total: 21.1s	remaining: 40.9s
340:	learn: 0.0436021	total: 21.1s	remaining: 40.9s
341:	learn: 0.0435595	total: 21.2s	remaining: 40.8s
342:	learn: 0.0435415	total: 21.2s	remaining: 40.7s
343:	learn: 0.0435092	total: 21.3s	remaining: 40.6s
344:	learn: 0.0434859	total: 21.4s	remaining: 40.6s
345:	learn: 0.0434200	total: 21.4s	remaining: 40.5s
346:	learn: 0.0433610	total: 21.5s	remaining: 40.4s
347:	learn: 0.0433164	total: 21.5s	remaining: 40.3s
348:	learn: 0.0432550	total: 21.6s	remaining: 40.3s
349:	learn: 0.0432403	total: 21.6s	remaining: 40.2s
350:	learn: 0.0432196	total: 21.7s	remaining: 40.1s
351:	learn: 0.0431998	total: 21.7s	remaining: 40s
352:	learn: 0.0431213	total: 21.8s	remaining: 40s
353:	learn: 0.0430731	total: 21.9s	remaining: 39.9s
354:	learn: 0.0429926	total: 22s	remaining: 39.9s
355:	learn: 0.0429516	total: 22s	remaining: 39.8s
356:	learn: 0.0428843	total: 22.1s	remaining: 39.8s
357:	learn: 0.0428037	total: 22.1s	remaining: 39.7s
358:	learn: 0.0427824	total: 22.2s	remaining: 39.6s
359:	learn: 0.0427373	total: 22.3s	remaining: 39.6s
360:	learn: 0.0427271	total: 22.3s	remaining: 39.5s
361:	learn: 0.0427096	total: 22.3s	remaining: 39.4s
362:	learn: 0.0426501	total: 22.4s	remaining: 39.3s
363:	learn: 0.0425881	total: 22.5s	remaining: 39.3s
364:	learn: 0.0425107	total: 22.5s	remaining: 39.2s
365:	learn: 0.0424256	total: 22.6s	remaining: 39.2s
366:	learn: 0.0424083	total: 22.7s	remaining: 39.1s
367:	learn: 0.0423970	total: 22.7s	remaining: 39s
368:	learn: 0.0423783	total: 22.8s	remaining: 38.9s
369:	learn: 0.0422953	total: 22.8s	remaining: 38.9s
370:	learn: 0.0422722	total: 22.9s	remaining: 38.8s
371:	learn: 0.0422205	total: 23s	remaining: 38.7s
372:	learn: 0.0421418	total: 23s	remaining: 38.7s
373:	learn: 0.0420832	total: 23.1s	remaining: 38.6s
374:	learn: 0.0420254	total: 23.1s	remaining: 38.6s
375:	learn: 0.0419662	total: 23.2s	remaining: 38.5s
376:	learn: 0.0419535	total: 23.2s	remaining: 38.4s
377:	learn: 0.0419365	total: 23.3s	remaining: 38.3s
378:	learn: 0.0419001	total: 23.4s	remaining: 38.3s
379:	learn: 0.0418013	total: 23.4s	remaining: 38.2s
380:	learn: 0.0417109	total: 23.5s	remaining: 38.2s
381:	learn: 0.0416704	total: 23.6s	remaining: 38.1s
382:	learn: 0.0416556	total: 23.6s	remaining: 38.1s
383:	learn: 0.0415758	total: 23.7s	remaining: 38s
384:	learn: 0.0415683	total: 23.7s	remaining: 37.9s
385:	learn: 0.0415102	total: 23.8s	remaining: 37.9s
386:	learn: 0.0414378	total: 23.9s	remaining: 37.8s
387:	learn: 0.0413830	total: 23.9s	remaining: 37.7s
388:	learn: 0.0413185	total: 24s	remaining: 37.7s
389:	learn: 0.0412950	total: 24s	remaining: 37.6s
390:	learn: 0.0412594	total: 24.1s	remaining: 37.5s
391:	learn: 0.0412492	total: 24.2s	remaining: 37.5s
392:	learn: 0.0411991	total: 24.2s	remaining: 37.4s
393:	learn: 0.0411253	total: 24.3s	remaining: 37.3s
394:	learn: 0.0410984	total: 24.3s	remaining: 37.3s
395:	learn: 0.0410676	total: 24.4s	remaining: 37.2s
396:	learn: 0.0409912	total: 24.5s	remaining: 37.1s
397:	learn: 0.0409437	total: 24.5s	remaining: 37.1s
398:	learn: 0.0409293	total: 24.6s	remaining: 37s
399:	learn: 0.0409001	total: 24.6s	remaining: 36.9s
400:	learn: 0.0408582	total: 24.7s	remaining: 36.9s
401:	learn: 0.0408160	total: 24.7s	remaining: 36.8s
402:	learn: 0.0408080	total: 24.8s	remaining: 36.7s
403:	learn: 0.0407404	total: 24.8s	remaining: 36.6s
404:	learn: 0.0407289	total: 24.9s	remaining: 36.6s
405:	learn: 0.0406754	total: 25s	remaining: 36.5s
406:	learn: 0.0406527	total: 25s	remaining: 36.4s
407:	learn: 0.0406024	total: 25.1s	remaining: 36.4s
408:	learn: 0.0405188	total: 25.1s	remaining: 36.3s
409:	learn: 0.0404624	total: 25.2s	remaining: 36.2s
410:	learn: 0.0404486	total: 25.2s	remaining: 36.2s
411:	learn: 0.0403791	total: 25.3s	remaining: 36.1s
412:	learn: 0.0403235	total: 25.4s	remaining: 36s
413:	learn: 0.0402013	total: 25.4s	remaining: 36s
414:	learn: 0.0401509	total: 25.5s	remaining: 35.9s
415:	learn: 0.0401406	total: 25.5s	remaining: 35.9s
416:	learn: 0.0400662	total: 25.6s	remaining: 35.8s
417:	learn: 0.0400170	total: 25.7s	remaining: 35.7s
418:	learn: 0.0399909	total: 25.7s	remaining: 35.7s
419:	learn: 0.0399411	total: 25.8s	remaining: 35.6s
420:	learn: 0.0398885	total: 25.8s	remaining: 35.5s
421:	learn: 0.0398724	total: 25.9s	remaining: 35.5s
422:	learn: 0.0398181	total: 26s	remaining: 35.4s
423:	learn: 0.0398041	total: 26s	remaining: 35.4s
424:	learn: 0.0397773	total: 26.1s	remaining: 35.3s
425:	learn: 0.0397024	total: 26.1s	remaining: 35.2s
426:	learn: 0.0396632	total: 26.2s	remaining: 35.1s
427:	learn: 0.0395964	total: 26.3s	remaining: 35.1s
428:	learn: 0.0395237	total: 26.3s	remaining: 35s
429:	learn: 0.0394551	total: 26.4s	remaining: 35s
430:	learn: 0.0393934	total: 26.4s	remaining: 34.9s
431:	learn: 0.0393817	total: 26.5s	remaining: 34.8s
432:	learn: 0.0393374	total: 26.5s	remaining: 34.8s
433:	learn: 0.0392755	total: 26.6s	remaining: 34.7s
434:	learn: 0.0392126	total: 26.7s	remaining: 34.7s
435:	learn: 0.0391985	total: 26.7s	remaining: 34.6s
436:	learn: 0.0391710	total: 26.8s	remaining: 34.5s
437:	learn: 0.0390880	total: 26.9s	remaining: 34.5s
438:	learn: 0.0390427	total: 26.9s	remaining: 34.4s
439:	learn: 0.0389617	total: 27s	remaining: 34.3s
440:	learn: 0.0389251	total: 27s	remaining: 34.3s
441:	learn: 0.0388939	total: 27.1s	remaining: 34.2s
442:	learn: 0.0388379	total: 27.2s	remaining: 34.2s
443:	learn: 0.0387952	total: 27.2s	remaining: 34.1s
444:	learn: 0.0387784	total: 27.3s	remaining: 34s
445:	learn: 0.0387161	total: 27.3s	remaining: 33.9s
446:	learn: 0.0385990	total: 27.4s	remaining: 33.9s
447:	learn: 0.0385369	total: 27.5s	remaining: 33.9s
448:	learn: 0.0384708	total: 27.5s	remaining: 33.8s
449:	learn: 0.0384438	total: 27.6s	remaining: 33.7s
450:	learn: 0.0384056	total: 27.6s	remaining: 33.7s
451:	learn: 0.0383957	total: 27.7s	remaining: 33.6s
452:	learn: 0.0383208	total: 27.8s	remaining: 33.5s
453:	learn: 0.0382553	total: 27.8s	remaining: 33.5s
454:	learn: 0.0382478	total: 27.9s	remaining: 33.4s
455:	learn: 0.0381889	total: 28s	remaining: 33.4s
456:	learn: 0.0380865	total: 28s	remaining: 33.3s
457:	learn: 0.0380553	total: 28.1s	remaining: 33.3s
458:	learn: 0.0380045	total: 28.2s	remaining: 33.2s
459:	learn: 0.0379596	total: 28.2s	remaining: 33.1s
460:	learn: 0.0378761	total: 28.3s	remaining: 33.1s
461:	learn: 0.0378627	total: 28.3s	remaining: 33s
462:	learn: 0.0377979	total: 28.4s	remaining: 32.9s
463:	learn: 0.0377713	total: 28.5s	remaining: 32.9s
464:	learn: 0.0377355	total: 28.5s	remaining: 32.8s
465:	learn: 0.0376651	total: 28.6s	remaining: 32.7s
466:	learn: 0.0376062	total: 28.6s	remaining: 32.7s
467:	learn: 0.0375431	total: 28.7s	remaining: 32.6s
468:	learn: 0.0374755	total: 28.8s	remaining: 32.6s
469:	learn: 0.0374064	total: 28.8s	remaining: 32.5s
470:	learn: 0.0373973	total: 28.9s	remaining: 32.4s
471:	learn: 0.0373512	total: 28.9s	remaining: 32.4s
472:	learn: 0.0373449	total: 29s	remaining: 32.3s
473:	learn: 0.0372688	total: 29s	remaining: 32.2s
474:	learn: 0.0372447	total: 29.1s	remaining: 32.2s
475:	learn: 0.0372011	total: 29.2s	remaining: 32.1s
476:	learn: 0.0371510	total: 29.2s	remaining: 32.1s
477:	learn: 0.0371112	total: 29.3s	remaining: 32s
478:	learn: 0.0370448	total: 29.4s	remaining: 31.9s
479:	learn: 0.0370131	total: 29.4s	remaining: 31.9s
480:	learn: 0.0369555	total: 29.5s	remaining: 31.8s
481:	learn: 0.0368922	total: 29.5s	remaining: 31.7s
482:	learn: 0.0368808	total: 29.6s	remaining: 31.7s
483:	learn: 0.0367946	total: 29.7s	remaining: 31.6s
484:	learn: 0.0367004	total: 29.7s	remaining: 31.6s
485:	learn: 0.0366180	total: 29.8s	remaining: 31.5s
486:	learn: 0.0365618	total: 29.9s	remaining: 31.4s
487:	learn: 0.0365438	total: 29.9s	remaining: 31.4s
488:	learn: 0.0365083	total: 30s	remaining: 31.3s
489:	learn: 0.0364557	total: 30s	remaining: 31.3s
490:	learn: 0.0364390	total: 30.1s	remaining: 31.2s
491:	learn: 0.0363935	total: 30.2s	remaining: 31.2s
492:	learn: 0.0363633	total: 30.2s	remaining: 31.1s
493:	learn: 0.0363525	total: 30.3s	remaining: 31s
494:	learn: 0.0363138	total: 30.4s	remaining: 31s
495:	learn: 0.0362942	total: 30.4s	remaining: 30.9s
496:	learn: 0.0362467	total: 30.5s	remaining: 30.8s
497:	learn: 0.0362093	total: 30.5s	remaining: 30.8s
498:	learn: 0.0361760	total: 30.6s	remaining: 30.7s
499:	learn: 0.0361259	total: 30.6s	remaining: 30.6s
500:	learn: 0.0360777	total: 30.7s	remaining: 30.6s
501:	learn: 0.0360636	total: 30.7s	remaining: 30.5s
502:	learn: 0.0360525	total: 30.8s	remaining: 30.4s
503:	learn: 0.0359696	total: 30.9s	remaining: 30.4s
504:	learn: 0.0359414	total: 30.9s	remaining: 30.3s
505:	learn: 0.0359156	total: 31s	remaining: 30.2s
506:	learn: 0.0358157	total: 31.1s	remaining: 30.2s
507:	learn: 0.0358096	total: 31.1s	remaining: 30.1s
508:	learn: 0.0357404	total: 31.2s	remaining: 30.1s
509:	learn: 0.0356759	total: 31.3s	remaining: 30s
510:	learn: 0.0356085	total: 31.3s	remaining: 30s
511:	learn: 0.0355876	total: 31.4s	remaining: 29.9s
512:	learn: 0.0355728	total: 31.4s	remaining: 29.9s
513:	learn: 0.0355323	total: 31.5s	remaining: 29.8s
514:	learn: 0.0355128	total: 31.5s	remaining: 29.7s
515:	learn: 0.0354479	total: 31.6s	remaining: 29.6s
516:	learn: 0.0354221	total: 31.7s	remaining: 29.6s
517:	learn: 0.0353789	total: 31.7s	remaining: 29.5s
518:	learn: 0.0353511	total: 31.8s	remaining: 29.5s
519:	learn: 0.0353201	total: 31.9s	remaining: 29.4s
520:	learn: 0.0353012	total: 31.9s	remaining: 29.3s
521:	learn: 0.0352626	total: 32s	remaining: 29.3s
522:	learn: 0.0352004	total: 32s	remaining: 29.2s
523:	learn: 0.0351243	total: 32.1s	remaining: 29.2s
524:	learn: 0.0350639	total: 32.2s	remaining: 29.1s
525:	learn: 0.0350380	total: 32.2s	remaining: 29.1s
526:	learn: 0.0350055	total: 32.3s	remaining: 29s
527:	learn: 0.0349969	total: 32.4s	remaining: 28.9s
528:	learn: 0.0349614	total: 32.4s	remaining: 28.9s
529:	learn: 0.0348880	total: 32.5s	remaining: 28.8s
530:	learn: 0.0348794	total: 32.5s	remaining: 28.7s
531:	learn: 0.0348443	total: 32.6s	remaining: 28.7s
532:	learn: 0.0348090	total: 32.6s	remaining: 28.6s
533:	learn: 0.0347245	total: 32.7s	remaining: 28.5s
534:	learn: 0.0346776	total: 32.8s	remaining: 28.5s
535:	learn: 0.0346415	total: 32.8s	remaining: 28.4s
536:	learn: 0.0345859	total: 32.9s	remaining: 28.4s
537:	learn: 0.0345575	total: 32.9s	remaining: 28.3s
538:	learn: 0.0345429	total: 33s	remaining: 28.2s
539:	learn: 0.0344806	total: 33.1s	remaining: 28.2s
540:	learn: 0.0344077	total: 33.1s	remaining: 28.1s
541:	learn: 0.0343885	total: 33.2s	remaining: 28.1s
542:	learn: 0.0343160	total: 33.3s	remaining: 28s
543:	learn: 0.0342627	total: 33.3s	remaining: 27.9s
544:	learn: 0.0342370	total: 33.4s	remaining: 27.9s
545:	learn: 0.0342262	total: 33.4s	remaining: 27.8s
546:	learn: 0.0342176	total: 33.5s	remaining: 27.7s
547:	learn: 0.0341867	total: 33.6s	remaining: 27.7s
548:	learn: 0.0341522	total: 33.6s	remaining: 27.6s
549:	learn: 0.0340983	total: 33.7s	remaining: 27.5s
550:	learn: 0.0340379	total: 33.7s	remaining: 27.5s
551:	learn: 0.0339870	total: 33.8s	remaining: 27.4s
552:	learn: 0.0339433	total: 33.9s	remaining: 27.4s
553:	learn: 0.0339118	total: 33.9s	remaining: 27.3s
554:	learn: 0.0338655	total: 34s	remaining: 27.2s
555:	learn: 0.0338326	total: 34.1s	remaining: 27.2s
556:	learn: 0.0337653	total: 34.1s	remaining: 27.2s
557:	learn: 0.0337196	total: 34.2s	remaining: 27.1s
558:	learn: 0.0336916	total: 34.3s	remaining: 27s
559:	learn: 0.0336423	total: 34.3s	remaining: 27s
560:	learn: 0.0336150	total: 34.4s	remaining: 26.9s
561:	learn: 0.0335597	total: 34.4s	remaining: 26.8s
562:	learn: 0.0335300	total: 34.5s	remaining: 26.8s
563:	learn: 0.0334988	total: 34.5s	remaining: 26.7s
564:	learn: 0.0334663	total: 34.6s	remaining: 26.6s
565:	learn: 0.0334272	total: 34.7s	remaining: 26.6s
566:	learn: 0.0333428	total: 34.7s	remaining: 26.5s
567:	learn: 0.0333380	total: 34.8s	remaining: 26.5s
568:	learn: 0.0333076	total: 34.8s	remaining: 26.4s
569:	learn: 0.0332927	total: 34.9s	remaining: 26.3s
570:	learn: 0.0332583	total: 35s	remaining: 26.3s
571:	learn: 0.0332366	total: 35s	remaining: 26.2s
572:	learn: 0.0331914	total: 35.1s	remaining: 26.1s
573:	learn: 0.0331626	total: 35.1s	remaining: 26.1s
574:	learn: 0.0331546	total: 35.2s	remaining: 26s
575:	learn: 0.0331077	total: 35.3s	remaining: 26s
576:	learn: 0.0330980	total: 35.3s	remaining: 25.9s
577:	learn: 0.0330618	total: 35.4s	remaining: 25.8s
578:	learn: 0.0330098	total: 35.5s	remaining: 25.8s
579:	learn: 0.0329898	total: 35.5s	remaining: 25.7s
580:	learn: 0.0329517	total: 35.6s	remaining: 25.7s
581:	learn: 0.0329261	total: 35.6s	remaining: 25.6s
582:	learn: 0.0328688	total: 35.7s	remaining: 25.5s
583:	learn: 0.0328177	total: 35.8s	remaining: 25.5s
584:	learn: 0.0327826	total: 35.8s	remaining: 25.4s
585:	learn: 0.0327660	total: 35.9s	remaining: 25.4s
586:	learn: 0.0327323	total: 35.9s	remaining: 25.3s
587:	learn: 0.0326937	total: 36s	remaining: 25.2s
588:	learn: 0.0326065	total: 36.1s	remaining: 25.2s
589:	learn: 0.0326018	total: 36.2s	remaining: 25.1s
590:	learn: 0.0325665	total: 36.2s	remaining: 25.1s
591:	learn: 0.0325439	total: 36.3s	remaining: 25s
592:	learn: 0.0325046	total: 36.3s	remaining: 24.9s
593:	learn: 0.0324523	total: 36.4s	remaining: 24.9s
594:	learn: 0.0324404	total: 36.5s	remaining: 24.8s
595:	learn: 0.0324021	total: 36.5s	remaining: 24.8s
596:	learn: 0.0323552	total: 36.6s	remaining: 24.7s
597:	learn: 0.0323432	total: 36.6s	remaining: 24.6s
598:	learn: 0.0322929	total: 36.7s	remaining: 24.6s
599:	learn: 0.0322434	total: 36.8s	remaining: 24.5s
600:	learn: 0.0322011	total: 36.8s	remaining: 24.4s
601:	learn: 0.0321716	total: 36.9s	remaining: 24.4s
602:	learn: 0.0321579	total: 36.9s	remaining: 24.3s
603:	learn: 0.0321053	total: 37s	remaining: 24.2s
604:	learn: 0.0320533	total: 37.1s	remaining: 24.2s
605:	learn: 0.0319672	total: 37.1s	remaining: 24.1s
606:	learn: 0.0319553	total: 37.2s	remaining: 24.1s
607:	learn: 0.0319023	total: 37.3s	remaining: 24s
608:	learn: 0.0318278	total: 37.4s	remaining: 24s
609:	learn: 0.0318037	total: 37.4s	remaining: 23.9s
610:	learn: 0.0317828	total: 37.5s	remaining: 23.9s
611:	learn: 0.0317351	total: 37.5s	remaining: 23.8s
612:	learn: 0.0317065	total: 37.6s	remaining: 23.7s
613:	learn: 0.0316657	total: 37.6s	remaining: 23.7s
614:	learn: 0.0316176	total: 37.7s	remaining: 23.6s
615:	learn: 0.0315764	total: 37.8s	remaining: 23.5s
616:	learn: 0.0315712	total: 37.8s	remaining: 23.5s
617:	learn: 0.0315607	total: 37.9s	remaining: 23.4s
618:	learn: 0.0315311	total: 37.9s	remaining: 23.3s
619:	learn: 0.0314843	total: 38s	remaining: 23.3s
620:	learn: 0.0314598	total: 38s	remaining: 23.2s
621:	learn: 0.0314288	total: 38.1s	remaining: 23.2s
622:	learn: 0.0313991	total: 38.2s	remaining: 23.1s
623:	learn: 0.0313426	total: 38.2s	remaining: 23s
624:	learn: 0.0313123	total: 38.3s	remaining: 23s
625:	learn: 0.0312812	total: 38.4s	remaining: 22.9s
626:	learn: 0.0312257	total: 38.4s	remaining: 22.9s
627:	learn: 0.0311804	total: 38.5s	remaining: 22.8s
628:	learn: 0.0311281	total: 38.6s	remaining: 22.8s
629:	learn: 0.0310975	total: 38.6s	remaining: 22.7s
630:	learn: 0.0310632	total: 38.7s	remaining: 22.6s
631:	learn: 0.0310540	total: 38.7s	remaining: 22.6s
632:	learn: 0.0310209	total: 38.8s	remaining: 22.5s
633:	learn: 0.0309755	total: 38.8s	remaining: 22.4s
634:	learn: 0.0309383	total: 38.9s	remaining: 22.4s
635:	learn: 0.0309008	total: 39s	remaining: 22.3s
636:	learn: 0.0308767	total: 39s	remaining: 22.2s
637:	learn: 0.0308441	total: 39.1s	remaining: 22.2s
638:	learn: 0.0308342	total: 39.1s	remaining: 22.1s
639:	learn: 0.0307876	total: 39.2s	remaining: 22s
640:	learn: 0.0307593	total: 39.2s	remaining: 22s
641:	learn: 0.0307308	total: 39.3s	remaining: 21.9s
642:	learn: 0.0306924	total: 39.4s	remaining: 21.9s
643:	learn: 0.0306766	total: 39.4s	remaining: 21.8s
644:	learn: 0.0306160	total: 39.5s	remaining: 21.7s
645:	learn: 0.0305915	total: 39.6s	remaining: 21.7s
646:	learn: 0.0305201	total: 39.6s	remaining: 21.6s
647:	learn: 0.0304827	total: 39.7s	remaining: 21.6s
648:	learn: 0.0304378	total: 39.8s	remaining: 21.5s
649:	learn: 0.0304251	total: 39.8s	remaining: 21.4s
650:	learn: 0.0303758	total: 39.9s	remaining: 21.4s
651:	learn: 0.0303563	total: 39.9s	remaining: 21.3s
652:	learn: 0.0303497	total: 40s	remaining: 21.3s
653:	learn: 0.0303230	total: 40s	remaining: 21.2s
654:	learn: 0.0303117	total: 40.1s	remaining: 21.1s
655:	learn: 0.0302554	total: 40.2s	remaining: 21.1s
656:	learn: 0.0302231	total: 40.2s	remaining: 21s
657:	learn: 0.0301755	total: 40.3s	remaining: 21s
658:	learn: 0.0301629	total: 40.4s	remaining: 20.9s
659:	learn: 0.0301400	total: 40.4s	remaining: 20.8s
660:	learn: 0.0301193	total: 40.5s	remaining: 20.8s
661:	learn: 0.0301137	total: 40.5s	remaining: 20.7s
662:	learn: 0.0301095	total: 40.6s	remaining: 20.6s
663:	learn: 0.0300937	total: 40.7s	remaining: 20.6s
664:	learn: 0.0300706	total: 40.7s	remaining: 20.5s
665:	learn: 0.0300278	total: 40.8s	remaining: 20.4s
666:	learn: 0.0300037	total: 40.8s	remaining: 20.4s
667:	learn: 0.0299649	total: 40.9s	remaining: 20.3s
668:	learn: 0.0299184	total: 41s	remaining: 20.3s
669:	learn: 0.0298970	total: 41s	remaining: 20.2s
670:	learn: 0.0298611	total: 41.1s	remaining: 20.2s
671:	learn: 0.0298283	total: 41.2s	remaining: 20.1s
672:	learn: 0.0298053	total: 41.2s	remaining: 20s
673:	learn: 0.0297496	total: 41.3s	remaining: 20s
674:	learn: 0.0297027	total: 41.4s	remaining: 19.9s
675:	learn: 0.0296553	total: 41.4s	remaining: 19.9s
676:	learn: 0.0296257	total: 41.5s	remaining: 19.8s
677:	learn: 0.0295857	total: 41.6s	remaining: 19.7s
678:	learn: 0.0295672	total: 41.6s	remaining: 19.7s
679:	learn: 0.0295428	total: 41.7s	remaining: 19.6s
680:	learn: 0.0294907	total: 41.7s	remaining: 19.6s
681:	learn: 0.0294445	total: 41.8s	remaining: 19.5s
682:	learn: 0.0294243	total: 41.9s	remaining: 19.4s
683:	learn: 0.0293903	total: 42s	remaining: 19.4s
684:	learn: 0.0293493	total: 42s	remaining: 19.3s
685:	learn: 0.0293238	total: 42.1s	remaining: 19.3s
686:	learn: 0.0293069	total: 42.2s	remaining: 19.2s
687:	learn: 0.0292978	total: 42.2s	remaining: 19.1s
688:	learn: 0.0292725	total: 42.3s	remaining: 19.1s
689:	learn: 0.0292627	total: 42.3s	remaining: 19s
690:	learn: 0.0292551	total: 42.4s	remaining: 19s
691:	learn: 0.0292011	total: 42.4s	remaining: 18.9s
692:	learn: 0.0291918	total: 42.5s	remaining: 18.8s
693:	learn: 0.0291396	total: 42.6s	remaining: 18.8s
694:	learn: 0.0291366	total: 42.6s	remaining: 18.7s
695:	learn: 0.0291198	total: 42.7s	remaining: 18.6s
696:	learn: 0.0290718	total: 42.7s	remaining: 18.6s
697:	learn: 0.0290376	total: 42.8s	remaining: 18.5s
698:	learn: 0.0290112	total: 42.9s	remaining: 18.5s
699:	learn: 0.0290086	total: 42.9s	remaining: 18.4s
700:	learn: 0.0289932	total: 43s	remaining: 18.3s
701:	learn: 0.0289627	total: 43s	remaining: 18.3s
702:	learn: 0.0289283	total: 43.1s	remaining: 18.2s
703:	learn: 0.0288679	total: 43.2s	remaining: 18.2s
704:	learn: 0.0288349	total: 43.3s	remaining: 18.1s
705:	learn: 0.0288050	total: 43.3s	remaining: 18s
706:	learn: 0.0287954	total: 43.4s	remaining: 18s
707:	learn: 0.0287631	total: 43.5s	remaining: 17.9s
708:	learn: 0.0287565	total: 43.5s	remaining: 17.9s
709:	learn: 0.0287446	total: 43.6s	remaining: 17.8s
710:	learn: 0.0287264	total: 43.6s	remaining: 17.7s
711:	learn: 0.0287128	total: 43.7s	remaining: 17.7s
712:	learn: 0.0286970	total: 43.8s	remaining: 17.6s
713:	learn: 0.0286573	total: 43.8s	remaining: 17.6s
714:	learn: 0.0286434	total: 43.9s	remaining: 17.5s
715:	learn: 0.0286327	total: 44s	remaining: 17.4s
716:	learn: 0.0286179	total: 44s	remaining: 17.4s
717:	learn: 0.0285974	total: 44.1s	remaining: 17.3s
718:	learn: 0.0285784	total: 44.1s	remaining: 17.2s
719:	learn: 0.0285372	total: 44.2s	remaining: 17.2s
720:	learn: 0.0285285	total: 44.2s	remaining: 17.1s
721:	learn: 0.0285204	total: 44.3s	remaining: 17.1s
722:	learn: 0.0284989	total: 44.4s	remaining: 17s
723:	learn: 0.0284667	total: 44.4s	remaining: 16.9s
724:	learn: 0.0284407	total: 44.5s	remaining: 16.9s
725:	learn: 0.0284078	total: 44.5s	remaining: 16.8s
726:	learn: 0.0283554	total: 44.6s	remaining: 16.8s
727:	learn: 0.0283208	total: 44.7s	remaining: 16.7s
728:	learn: 0.0282777	total: 44.7s	remaining: 16.6s
729:	learn: 0.0282488	total: 44.8s	remaining: 16.6s
730:	learn: 0.0282019	total: 44.9s	remaining: 16.5s
731:	learn: 0.0281585	total: 44.9s	remaining: 16.4s
732:	learn: 0.0281426	total: 45s	remaining: 16.4s
733:	learn: 0.0280872	total: 45s	remaining: 16.3s
734:	learn: 0.0280812	total: 45.1s	remaining: 16.3s
735:	learn: 0.0280551	total: 45.1s	remaining: 16.2s
736:	learn: 0.0279998	total: 45.2s	remaining: 16.1s
737:	learn: 0.0279722	total: 45.3s	remaining: 16.1s
738:	learn: 0.0279631	total: 45.3s	remaining: 16s
739:	learn: 0.0279177	total: 45.4s	remaining: 16s
740:	learn: 0.0279066	total: 45.5s	remaining: 15.9s
741:	learn: 0.0278711	total: 45.5s	remaining: 15.8s
742:	learn: 0.0278474	total: 45.6s	remaining: 15.8s
743:	learn: 0.0278237	total: 45.7s	remaining: 15.7s
744:	learn: 0.0277704	total: 45.7s	remaining: 15.7s
745:	learn: 0.0277472	total: 45.8s	remaining: 15.6s
746:	learn: 0.0277098	total: 45.9s	remaining: 15.5s
747:	learn: 0.0276644	total: 45.9s	remaining: 15.5s
748:	learn: 0.0276414	total: 46s	remaining: 15.4s
749:	learn: 0.0276266	total: 46.1s	remaining: 15.4s
750:	learn: 0.0276001	total: 46.1s	remaining: 15.3s
751:	learn: 0.0275916	total: 46.2s	remaining: 15.2s
752:	learn: 0.0275847	total: 46.2s	remaining: 15.2s
753:	learn: 0.0275727	total: 46.3s	remaining: 15.1s
754:	learn: 0.0275675	total: 46.3s	remaining: 15s
755:	learn: 0.0275525	total: 46.4s	remaining: 15s
756:	learn: 0.0275229	total: 46.5s	remaining: 14.9s
757:	learn: 0.0274909	total: 46.5s	remaining: 14.8s
758:	learn: 0.0274722	total: 46.6s	remaining: 14.8s
759:	learn: 0.0274257	total: 46.6s	remaining: 14.7s
760:	learn: 0.0273975	total: 46.7s	remaining: 14.7s
761:	learn: 0.0273831	total: 46.7s	remaining: 14.6s
762:	learn: 0.0273491	total: 46.8s	remaining: 14.5s
763:	learn: 0.0272865	total: 46.9s	remaining: 14.5s
764:	learn: 0.0272786	total: 46.9s	remaining: 14.4s
765:	learn: 0.0272639	total: 47s	remaining: 14.4s
766:	learn: 0.0272402	total: 47.1s	remaining: 14.3s
767:	learn: 0.0271794	total: 47.1s	remaining: 14.2s
768:	learn: 0.0271474	total: 47.2s	remaining: 14.2s
769:	learn: 0.0271092	total: 47.2s	remaining: 14.1s
770:	learn: 0.0270578	total: 47.3s	remaining: 14s
771:	learn: 0.0270511	total: 47.3s	remaining: 14s
772:	learn: 0.0270312	total: 47.4s	remaining: 13.9s
773:	learn: 0.0269960	total: 47.5s	remaining: 13.9s
774:	learn: 0.0269798	total: 47.5s	remaining: 13.8s
775:	learn: 0.0269584	total: 47.6s	remaining: 13.7s
776:	learn: 0.0269204	total: 47.7s	remaining: 13.7s
777:	learn: 0.0268953	total: 47.7s	remaining: 13.6s
778:	learn: 0.0268695	total: 47.8s	remaining: 13.6s
779:	learn: 0.0268229	total: 47.9s	remaining: 13.5s
780:	learn: 0.0267775	total: 47.9s	remaining: 13.4s
781:	learn: 0.0267449	total: 48s	remaining: 13.4s
782:	learn: 0.0266994	total: 48s	remaining: 13.3s
783:	learn: 0.0266727	total: 48.1s	remaining: 13.3s
784:	learn: 0.0266427	total: 48.2s	remaining: 13.2s
785:	learn: 0.0266290	total: 48.2s	remaining: 13.1s
786:	learn: 0.0266109	total: 48.3s	remaining: 13.1s
787:	learn: 0.0265984	total: 48.4s	remaining: 13s
788:	learn: 0.0265934	total: 48.4s	remaining: 12.9s
789:	learn: 0.0265414	total: 48.5s	remaining: 12.9s
790:	learn: 0.0265127	total: 48.5s	remaining: 12.8s
791:	learn: 0.0264583	total: 48.6s	remaining: 12.8s
792:	learn: 0.0264244	total: 48.7s	remaining: 12.7s
793:	learn: 0.0264190	total: 48.7s	remaining: 12.6s
794:	learn: 0.0264089	total: 48.8s	remaining: 12.6s
795:	learn: 0.0263872	total: 48.8s	remaining: 12.5s
796:	learn: 0.0263585	total: 48.9s	remaining: 12.5s
797:	learn: 0.0263388	total: 49s	remaining: 12.4s
798:	learn: 0.0263052	total: 49s	remaining: 12.3s
799:	learn: 0.0262648	total: 49.1s	remaining: 12.3s
800:	learn: 0.0262507	total: 49.2s	remaining: 12.2s
801:	learn: 0.0262390	total: 49.2s	remaining: 12.1s
802:	learn: 0.0262239	total: 49.3s	remaining: 12.1s
803:	learn: 0.0262049	total: 49.3s	remaining: 12s
804:	learn: 0.0262003	total: 49.4s	remaining: 12s
805:	learn: 0.0261573	total: 49.4s	remaining: 11.9s
806:	learn: 0.0261318	total: 49.5s	remaining: 11.8s
807:	learn: 0.0261285	total: 49.6s	remaining: 11.8s
808:	learn: 0.0260962	total: 49.6s	remaining: 11.7s
809:	learn: 0.0260890	total: 49.7s	remaining: 11.7s
810:	learn: 0.0260727	total: 49.7s	remaining: 11.6s
811:	learn: 0.0260418	total: 49.8s	remaining: 11.5s
812:	learn: 0.0260079	total: 49.9s	remaining: 11.5s
813:	learn: 0.0260031	total: 49.9s	remaining: 11.4s
814:	learn: 0.0259943	total: 50s	remaining: 11.3s
815:	learn: 0.0259824	total: 50s	remaining: 11.3s
816:	learn: 0.0259299	total: 50.1s	remaining: 11.2s
817:	learn: 0.0259143	total: 50.1s	remaining: 11.2s
818:	learn: 0.0258835	total: 50.2s	remaining: 11.1s
819:	learn: 0.0258621	total: 50.3s	remaining: 11s
820:	learn: 0.0258194	total: 50.3s	remaining: 11s
821:	learn: 0.0258148	total: 50.4s	remaining: 10.9s
822:	learn: 0.0257794	total: 50.5s	remaining: 10.9s
823:	learn: 0.0257682	total: 50.5s	remaining: 10.8s
824:	learn: 0.0257262	total: 50.6s	remaining: 10.7s
825:	learn: 0.0257001	total: 50.6s	remaining: 10.7s
826:	learn: 0.0256724	total: 50.7s	remaining: 10.6s
827:	learn: 0.0256528	total: 50.8s	remaining: 10.5s
828:	learn: 0.0256438	total: 50.8s	remaining: 10.5s
829:	learn: 0.0256167	total: 50.9s	remaining: 10.4s
830:	learn: 0.0255667	total: 51s	remaining: 10.4s
831:	learn: 0.0255246	total: 51s	remaining: 10.3s
832:	learn: 0.0254934	total: 51.1s	remaining: 10.2s
833:	learn: 0.0254782	total: 51.1s	remaining: 10.2s
834:	learn: 0.0254505	total: 51.2s	remaining: 10.1s
835:	learn: 0.0254398	total: 51.3s	remaining: 10.1s
836:	learn: 0.0254037	total: 51.3s	remaining: 9.99s
837:	learn: 0.0253910	total: 51.4s	remaining: 9.93s
838:	learn: 0.0253812	total: 51.4s	remaining: 9.87s
839:	learn: 0.0253228	total: 51.5s	remaining: 9.81s
840:	learn: 0.0252741	total: 51.6s	remaining: 9.75s
841:	learn: 0.0252451	total: 51.6s	remaining: 9.69s
842:	learn: 0.0252142	total: 51.7s	remaining: 9.63s
843:	learn: 0.0252018	total: 51.8s	remaining: 9.57s
844:	learn: 0.0251750	total: 51.8s	remaining: 9.51s
845:	learn: 0.0251648	total: 51.9s	remaining: 9.44s
846:	learn: 0.0251309	total: 51.9s	remaining: 9.38s
847:	learn: 0.0251211	total: 52s	remaining: 9.32s
848:	learn: 0.0250934	total: 52.1s	remaining: 9.26s
849:	learn: 0.0250676	total: 52.1s	remaining: 9.2s
850:	learn: 0.0250629	total: 52.2s	remaining: 9.14s
851:	learn: 0.0250516	total: 52.2s	remaining: 9.07s
852:	learn: 0.0250339	total: 52.3s	remaining: 9.01s
853:	learn: 0.0250043	total: 52.4s	remaining: 8.95s
854:	learn: 0.0249713	total: 52.4s	remaining: 8.89s
855:	learn: 0.0249678	total: 52.5s	remaining: 8.83s
856:	learn: 0.0249354	total: 52.6s	remaining: 8.77s
857:	learn: 0.0249276	total: 52.6s	remaining: 8.71s
858:	learn: 0.0249022	total: 52.7s	remaining: 8.64s
859:	learn: 0.0248951	total: 52.7s	remaining: 8.58s
860:	learn: 0.0248896	total: 52.8s	remaining: 8.52s
861:	learn: 0.0248823	total: 52.8s	remaining: 8.46s
862:	learn: 0.0248503	total: 52.9s	remaining: 8.39s
863:	learn: 0.0248426	total: 52.9s	remaining: 8.33s
864:	learn: 0.0248109	total: 53s	remaining: 8.27s
865:	learn: 0.0247944	total: 53.1s	remaining: 8.21s
866:	learn: 0.0247711	total: 53.1s	remaining: 8.15s
867:	learn: 0.0247424	total: 53.2s	remaining: 8.09s
868:	learn: 0.0247025	total: 53.2s	remaining: 8.03s
869:	learn: 0.0246955	total: 53.3s	remaining: 7.96s
870:	learn: 0.0246729	total: 53.4s	remaining: 7.9s
871:	learn: 0.0246534	total: 53.4s	remaining: 7.84s
872:	learn: 0.0246492	total: 53.5s	remaining: 7.78s
873:	learn: 0.0246279	total: 53.5s	remaining: 7.72s
874:	learn: 0.0246170	total: 53.6s	remaining: 7.66s
875:	learn: 0.0245815	total: 53.7s	remaining: 7.59s
876:	learn: 0.0245520	total: 53.7s	remaining: 7.53s
877:	learn: 0.0245502	total: 53.8s	remaining: 7.47s
878:	learn: 0.0245062	total: 53.8s	remaining: 7.41s
879:	learn: 0.0244774	total: 53.9s	remaining: 7.35s
880:	learn: 0.0244436	total: 54s	remaining: 7.29s
881:	learn: 0.0244210	total: 54s	remaining: 7.23s
882:	learn: 0.0243850	total: 54.1s	remaining: 7.17s
883:	learn: 0.0243285	total: 54.2s	remaining: 7.11s
884:	learn: 0.0243017	total: 54.2s	remaining: 7.05s
885:	learn: 0.0242775	total: 54.3s	remaining: 6.99s
886:	learn: 0.0242603	total: 54.4s	remaining: 6.92s
887:	learn: 0.0242514	total: 54.4s	remaining: 6.86s
888:	learn: 0.0242282	total: 54.5s	remaining: 6.8s
889:	learn: 0.0242102	total: 54.5s	remaining: 6.74s
890:	learn: 0.0241663	total: 54.6s	remaining: 6.68s
891:	learn: 0.0241549	total: 54.7s	remaining: 6.62s
892:	learn: 0.0241232	total: 54.7s	remaining: 6.56s
893:	learn: 0.0241109	total: 54.8s	remaining: 6.5s
894:	learn: 0.0240741	total: 54.9s	remaining: 6.43s
895:	learn: 0.0240525	total: 54.9s	remaining: 6.37s
896:	learn: 0.0240249	total: 55s	remaining: 6.31s
897:	learn: 0.0240034	total: 55s	remaining: 6.25s
898:	learn: 0.0239935	total: 55.1s	remaining: 6.19s
899:	learn: 0.0239772	total: 55.2s	remaining: 6.13s
900:	learn: 0.0239706	total: 55.2s	remaining: 6.07s
901:	learn: 0.0239671	total: 55.3s	remaining: 6s
902:	learn: 0.0239353	total: 55.3s	remaining: 5.94s
903:	learn: 0.0239165	total: 55.4s	remaining: 5.88s
904:	learn: 0.0239114	total: 55.4s	remaining: 5.82s
905:	learn: 0.0238832	total: 55.5s	remaining: 5.76s
906:	learn: 0.0238672	total: 55.5s	remaining: 5.69s
907:	learn: 0.0238257	total: 55.6s	remaining: 5.63s
908:	learn: 0.0238165	total: 55.7s	remaining: 5.57s
909:	learn: 0.0238013	total: 55.7s	remaining: 5.51s
910:	learn: 0.0237823	total: 55.8s	remaining: 5.45s
911:	learn: 0.0237504	total: 55.9s	remaining: 5.39s
912:	learn: 0.0237445	total: 55.9s	remaining: 5.33s
913:	learn: 0.0237185	total: 56s	remaining: 5.27s
914:	learn: 0.0236729	total: 56s	remaining: 5.21s
915:	learn: 0.0236692	total: 56.1s	remaining: 5.14s
916:	learn: 0.0236532	total: 56.1s	remaining: 5.08s
917:	learn: 0.0236347	total: 56.2s	remaining: 5.02s
918:	learn: 0.0235988	total: 56.3s	remaining: 4.96s
919:	learn: 0.0235596	total: 56.3s	remaining: 4.9s
920:	learn: 0.0235518	total: 56.4s	remaining: 4.84s
921:	learn: 0.0235293	total: 56.4s	remaining: 4.78s
922:	learn: 0.0235031	total: 56.5s	remaining: 4.71s
923:	learn: 0.0234717	total: 56.6s	remaining: 4.65s
924:	learn: 0.0234507	total: 56.6s	remaining: 4.59s
925:	learn: 0.0234292	total: 56.7s	remaining: 4.53s
926:	learn: 0.0234076	total: 56.8s	remaining: 4.47s
927:	learn: 0.0233658	total: 56.8s	remaining: 4.41s
928:	learn: 0.0233130	total: 56.9s	remaining: 4.35s
929:	learn: 0.0233093	total: 57s	remaining: 4.29s
930:	learn: 0.0232671	total: 57s	remaining: 4.23s
931:	learn: 0.0232418	total: 57.1s	remaining: 4.17s
932:	learn: 0.0232256	total: 57.1s	remaining: 4.1s
933:	learn: 0.0232210	total: 57.2s	remaining: 4.04s
934:	learn: 0.0231920	total: 57.3s	remaining: 3.98s
935:	learn: 0.0231802	total: 57.3s	remaining: 3.92s
936:	learn: 0.0231556	total: 57.4s	remaining: 3.86s
937:	learn: 0.0231224	total: 57.4s	remaining: 3.8s
938:	learn: 0.0231191	total: 57.5s	remaining: 3.73s
939:	learn: 0.0230742	total: 57.6s	remaining: 3.67s
940:	learn: 0.0230651	total: 57.6s	remaining: 3.61s
941:	learn: 0.0230574	total: 57.7s	remaining: 3.55s
942:	learn: 0.0230488	total: 57.7s	remaining: 3.49s
943:	learn: 0.0230256	total: 57.8s	remaining: 3.43s
944:	learn: 0.0230000	total: 57.9s	remaining: 3.37s
945:	learn: 0.0229872	total: 57.9s	remaining: 3.31s
946:	learn: 0.0229844	total: 58s	remaining: 3.24s
947:	learn: 0.0229767	total: 58s	remaining: 3.18s
948:	learn: 0.0229706	total: 58.1s	remaining: 3.12s
949:	learn: 0.0229672	total: 58.1s	remaining: 3.06s
950:	learn: 0.0229439	total: 58.2s	remaining: 3s
951:	learn: 0.0229263	total: 58.2s	remaining: 2.94s
952:	learn: 0.0229192	total: 58.3s	remaining: 2.88s
953:	learn: 0.0229141	total: 58.4s	remaining: 2.81s
954:	learn: 0.0229043	total: 58.4s	remaining: 2.75s
955:	learn: 0.0228803	total: 58.5s	remaining: 2.69s
956:	learn: 0.0228587	total: 58.5s	remaining: 2.63s
957:	learn: 0.0228561	total: 58.6s	remaining: 2.57s
958:	learn: 0.0228410	total: 58.7s	remaining: 2.51s
959:	learn: 0.0228141	total: 58.7s	remaining: 2.45s
960:	learn: 0.0228071	total: 58.8s	remaining: 2.38s
961:	learn: 0.0227849	total: 58.9s	remaining: 2.32s
962:	learn: 0.0227770	total: 58.9s	remaining: 2.26s
963:	learn: 0.0227464	total: 59s	remaining: 2.2s
964:	learn: 0.0227197	total: 59s	remaining: 2.14s
965:	learn: 0.0227046	total: 59.1s	remaining: 2.08s
966:	learn: 0.0226993	total: 59.1s	remaining: 2.02s
967:	learn: 0.0226579	total: 59.2s	remaining: 1.96s
968:	learn: 0.0226347	total: 59.3s	remaining: 1.9s
969:	learn: 0.0226147	total: 59.4s	remaining: 1.83s
970:	learn: 0.0226017	total: 59.4s	remaining: 1.77s
971:	learn: 0.0225541	total: 59.5s	remaining: 1.71s
972:	learn: 0.0225493	total: 59.5s	remaining: 1.65s
973:	learn: 0.0225434	total: 59.6s	remaining: 1.59s
974:	learn: 0.0225364	total: 59.7s	remaining: 1.53s
975:	learn: 0.0225068	total: 59.7s	remaining: 1.47s
976:	learn: 0.0224947	total: 59.8s	remaining: 1.41s
977:	learn: 0.0224910	total: 59.8s	remaining: 1.35s
978:	learn: 0.0224615	total: 59.9s	remaining: 1.28s
979:	learn: 0.0224490	total: 60s	remaining: 1.22s
980:	learn: 0.0224443	total: 1m	remaining: 1.16s
981:	learn: 0.0224229	total: 1m	remaining: 1.1s
982:	learn: 0.0224057	total: 1m	remaining: 1.04s
983:	learn: 0.0223685	total: 1m	remaining: 979ms
984:	learn: 0.0223444	total: 1m	remaining: 918ms
985:	learn: 0.0223207	total: 1m	remaining: 857ms
986:	learn: 0.0223146	total: 1m	remaining: 796ms
987:	learn: 0.0222785	total: 1m	remaining: 735ms
988:	learn: 0.0222646	total: 1m	remaining: 674ms
989:	learn: 0.0222504	total: 1m	remaining: 612ms
990:	learn: 0.0222295	total: 1m	remaining: 551ms
991:	learn: 0.0222056	total: 1m	remaining: 490ms
992:	learn: 0.0221826	total: 1m	remaining: 429ms
993:	learn: 0.0221635	total: 1m	remaining: 368ms
994:	learn: 0.0221512	total: 1m	remaining: 306ms
995:	learn: 0.0221278	total: 1m 1s	remaining: 245ms
996:	learn: 0.0221038	total: 1m 1s	remaining: 184ms
997:	learn: 0.0220950	total: 1m 1s	remaining: 123ms
998:	learn: 0.0220828	total: 1m 1s	remaining: 61.3ms
999:	learn: 0.0220734	total: 1m 1s	remaining: 0us
Learning rate set to 0.051317
0:	learn: 0.5897187	total: 76.1ms	remaining: 1m 16s
1:	learn: 0.5025509	total: 173ms	remaining: 1m 26s
2:	learn: 0.4304094	total: 250ms	remaining: 1m 23s
3:	learn: 0.3700185	total: 331ms	remaining: 1m 22s
4:	learn: 0.3206923	total: 398ms	remaining: 1m 19s
5:	learn: 0.2797247	total: 486ms	remaining: 1m 20s
6:	learn: 0.2453639	total: 584ms	remaining: 1m 22s
7:	learn: 0.2165853	total: 665ms	remaining: 1m 22s
8:	learn: 0.1934635	total: 740ms	remaining: 1m 21s
9:	learn: 0.1739903	total: 818ms	remaining: 1m 20s
10:	learn: 0.1569846	total: 900ms	remaining: 1m 20s
11:	learn: 0.1436245	total: 1s	remaining: 1m 22s
12:	learn: 0.1324244	total: 1.08s	remaining: 1m 22s
13:	learn: 0.1228579	total: 1.16s	remaining: 1m 21s
14:	learn: 0.1149009	total: 1.25s	remaining: 1m 22s
15:	learn: 0.1080251	total: 1.35s	remaining: 1m 22s
16:	learn: 0.1022480	total: 1.43s	remaining: 1m 22s
17:	learn: 0.0972607	total: 1.52s	remaining: 1m 23s
18:	learn: 0.0930019	total: 1.61s	remaining: 1m 22s
19:	learn: 0.0894040	total: 1.68s	remaining: 1m 22s
20:	learn: 0.0862990	total: 1.77s	remaining: 1m 22s
21:	learn: 0.0836352	total: 1.85s	remaining: 1m 22s
22:	learn: 0.0812284	total: 1.94s	remaining: 1m 22s
23:	learn: 0.0792764	total: 2s	remaining: 1m 21s
24:	learn: 0.0775227	total: 2.07s	remaining: 1m 20s
25:	learn: 0.0754167	total: 2.15s	remaining: 1m 20s
26:	learn: 0.0740957	total: 2.22s	remaining: 1m 20s
27:	learn: 0.0729224	total: 2.31s	remaining: 1m 20s
28:	learn: 0.0719059	total: 2.39s	remaining: 1m 20s
29:	learn: 0.0700746	total: 2.49s	remaining: 1m 20s
30:	learn: 0.0693127	total: 2.57s	remaining: 1m 20s
31:	learn: 0.0686438	total: 2.68s	remaining: 1m 21s
32:	learn: 0.0680709	total: 2.76s	remaining: 1m 20s
33:	learn: 0.0667455	total: 2.84s	remaining: 1m 20s
34:	learn: 0.0662401	total: 2.92s	remaining: 1m 20s
35:	learn: 0.0651977	total: 3s	remaining: 1m 20s
36:	learn: 0.0645936	total: 3.08s	remaining: 1m 20s
37:	learn: 0.0642867	total: 3.17s	remaining: 1m 20s
38:	learn: 0.0640421	total: 3.25s	remaining: 1m 20s
39:	learn: 0.0634410	total: 3.33s	remaining: 1m 20s
40:	learn: 0.0627090	total: 3.42s	remaining: 1m 20s
41:	learn: 0.0621020	total: 3.51s	remaining: 1m 19s
42:	learn: 0.0619734	total: 3.59s	remaining: 1m 19s
43:	learn: 0.0617212	total: 3.66s	remaining: 1m 19s
44:	learn: 0.0615149	total: 3.75s	remaining: 1m 19s
45:	learn: 0.0611498	total: 3.83s	remaining: 1m 19s
46:	learn: 0.0609068	total: 3.92s	remaining: 1m 19s
47:	learn: 0.0607916	total: 3.98s	remaining: 1m 18s
48:	learn: 0.0606808	total: 4.05s	remaining: 1m 18s
49:	learn: 0.0605913	total: 4.12s	remaining: 1m 18s
50:	learn: 0.0602472	total: 4.22s	remaining: 1m 18s
51:	learn: 0.0601428	total: 4.29s	remaining: 1m 18s
52:	learn: 0.0599666	total: 4.37s	remaining: 1m 17s
53:	learn: 0.0596775	total: 4.45s	remaining: 1m 17s
54:	learn: 0.0593883	total: 4.53s	remaining: 1m 17s
55:	learn: 0.0591556	total: 4.63s	remaining: 1m 18s
56:	learn: 0.0591160	total: 4.69s	remaining: 1m 17s
57:	learn: 0.0589182	total: 4.77s	remaining: 1m 17s
58:	learn: 0.0588578	total: 4.84s	remaining: 1m 17s
59:	learn: 0.0587280	total: 4.93s	remaining: 1m 17s
60:	learn: 0.0587011	total: 4.99s	remaining: 1m 16s
61:	learn: 0.0586839	total: 5.06s	remaining: 1m 16s
62:	learn: 0.0586421	total: 5.13s	remaining: 1m 16s
63:	learn: 0.0586106	total: 5.21s	remaining: 1m 16s
64:	learn: 0.0585856	total: 5.28s	remaining: 1m 15s
65:	learn: 0.0584046	total: 5.36s	remaining: 1m 15s
66:	learn: 0.0583879	total: 5.42s	remaining: 1m 15s
67:	learn: 0.0583560	total: 5.48s	remaining: 1m 15s
68:	learn: 0.0583071	total: 5.56s	remaining: 1m 15s
69:	learn: 0.0582921	total: 5.64s	remaining: 1m 14s
70:	learn: 0.0582786	total: 5.72s	remaining: 1m 14s
71:	learn: 0.0582653	total: 5.8s	remaining: 1m 14s
72:	learn: 0.0582441	total: 5.87s	remaining: 1m 14s
73:	learn: 0.0582249	total: 5.95s	remaining: 1m 14s
74:	learn: 0.0580656	total: 6.03s	remaining: 1m 14s
75:	learn: 0.0580585	total: 6.09s	remaining: 1m 14s
76:	learn: 0.0579495	total: 6.16s	remaining: 1m 13s
77:	learn: 0.0579263	total: 6.23s	remaining: 1m 13s
78:	learn: 0.0579154	total: 6.3s	remaining: 1m 13s
79:	learn: 0.0579021	total: 6.37s	remaining: 1m 13s
80:	learn: 0.0577682	total: 6.45s	remaining: 1m 13s
81:	learn: 0.0577577	total: 6.53s	remaining: 1m 13s
82:	learn: 0.0576173	total: 6.6s	remaining: 1m 12s
83:	learn: 0.0575688	total: 6.7s	remaining: 1m 13s
84:	learn: 0.0575437	total: 6.76s	remaining: 1m 12s
85:	learn: 0.0575375	total: 6.83s	remaining: 1m 12s
86:	learn: 0.0575240	total: 6.9s	remaining: 1m 12s
87:	learn: 0.0575125	total: 6.97s	remaining: 1m 12s
88:	learn: 0.0574024	total: 7.06s	remaining: 1m 12s
89:	learn: 0.0572824	total: 7.14s	remaining: 1m 12s
90:	learn: 0.0571895	total: 7.23s	remaining: 1m 12s
91:	learn: 0.0571371	total: 7.3s	remaining: 1m 12s
92:	learn: 0.0571212	total: 7.37s	remaining: 1m 11s
93:	learn: 0.0569974	total: 7.45s	remaining: 1m 11s
94:	learn: 0.0569156	total: 7.51s	remaining: 1m 11s
95:	learn: 0.0569033	total: 7.58s	remaining: 1m 11s
96:	learn: 0.0568504	total: 7.68s	remaining: 1m 11s
97:	learn: 0.0567691	total: 7.77s	remaining: 1m 11s
98:	learn: 0.0566392	total: 7.86s	remaining: 1m 11s
99:	learn: 0.0566210	total: 7.93s	remaining: 1m 11s
100:	learn: 0.0565781	total: 8s	remaining: 1m 11s
101:	learn: 0.0565534	total: 8.06s	remaining: 1m 10s
102:	learn: 0.0564837	total: 8.12s	remaining: 1m 10s
103:	learn: 0.0564679	total: 8.18s	remaining: 1m 10s
104:	learn: 0.0564505	total: 8.25s	remaining: 1m 10s
105:	learn: 0.0563834	total: 8.33s	remaining: 1m 10s
106:	learn: 0.0563479	total: 8.41s	remaining: 1m 10s
107:	learn: 0.0563358	total: 8.46s	remaining: 1m 9s
108:	learn: 0.0562801	total: 8.54s	remaining: 1m 9s
109:	learn: 0.0562091	total: 8.63s	remaining: 1m 9s
110:	learn: 0.0561562	total: 8.69s	remaining: 1m 9s
111:	learn: 0.0561121	total: 8.78s	remaining: 1m 9s
112:	learn: 0.0560517	total: 8.85s	remaining: 1m 9s
113:	learn: 0.0559922	total: 8.91s	remaining: 1m 9s
114:	learn: 0.0558870	total: 8.99s	remaining: 1m 9s
115:	learn: 0.0558276	total: 9.07s	remaining: 1m 9s
116:	learn: 0.0557097	total: 9.15s	remaining: 1m 9s
117:	learn: 0.0556353	total: 9.21s	remaining: 1m 8s
118:	learn: 0.0555682	total: 9.29s	remaining: 1m 8s
119:	learn: 0.0555433	total: 9.35s	remaining: 1m 8s
120:	learn: 0.0555313	total: 9.41s	remaining: 1m 8s
121:	learn: 0.0554885	total: 9.48s	remaining: 1m 8s
122:	learn: 0.0554738	total: 9.56s	remaining: 1m 8s
123:	learn: 0.0554241	total: 9.62s	remaining: 1m 7s
124:	learn: 0.0553689	total: 9.68s	remaining: 1m 7s
125:	learn: 0.0553434	total: 9.75s	remaining: 1m 7s
126:	learn: 0.0552818	total: 9.82s	remaining: 1m 7s
127:	learn: 0.0552433	total: 9.92s	remaining: 1m 7s
128:	learn: 0.0552181	total: 9.99s	remaining: 1m 7s
129:	learn: 0.0551977	total: 10.1s	remaining: 1m 7s
130:	learn: 0.0551234	total: 10.1s	remaining: 1m 7s
131:	learn: 0.0550688	total: 10.2s	remaining: 1m 6s
132:	learn: 0.0550324	total: 10.2s	remaining: 1m 6s
133:	learn: 0.0549872	total: 10.3s	remaining: 1m 6s
134:	learn: 0.0549787	total: 10.4s	remaining: 1m 6s
135:	learn: 0.0549338	total: 10.4s	remaining: 1m 6s
136:	learn: 0.0549042	total: 10.5s	remaining: 1m 6s
137:	learn: 0.0548229	total: 10.6s	remaining: 1m 6s
138:	learn: 0.0547881	total: 10.7s	remaining: 1m 5s
139:	learn: 0.0547231	total: 10.7s	remaining: 1m 5s
140:	learn: 0.0546403	total: 10.8s	remaining: 1m 6s
141:	learn: 0.0546048	total: 10.9s	remaining: 1m 5s
142:	learn: 0.0545660	total: 11s	remaining: 1m 5s
143:	learn: 0.0545151	total: 11.1s	remaining: 1m 5s
144:	learn: 0.0544845	total: 11.1s	remaining: 1m 5s
145:	learn: 0.0544406	total: 11.2s	remaining: 1m 5s
146:	learn: 0.0544097	total: 11.3s	remaining: 1m 5s
147:	learn: 0.0543577	total: 11.3s	remaining: 1m 5s
148:	learn: 0.0543012	total: 11.4s	remaining: 1m 5s
149:	learn: 0.0542533	total: 11.5s	remaining: 1m 5s
150:	learn: 0.0541871	total: 11.6s	remaining: 1m 4s
151:	learn: 0.0541105	total: 11.6s	remaining: 1m 4s
152:	learn: 0.0540593	total: 11.7s	remaining: 1m 4s
153:	learn: 0.0539530	total: 11.8s	remaining: 1m 4s
154:	learn: 0.0538421	total: 11.9s	remaining: 1m 5s
155:	learn: 0.0537815	total: 12s	remaining: 1m 5s
156:	learn: 0.0537648	total: 12.1s	remaining: 1m 4s
157:	learn: 0.0537238	total: 12.2s	remaining: 1m 4s
158:	learn: 0.0536763	total: 12.2s	remaining: 1m 4s
159:	learn: 0.0536120	total: 12.3s	remaining: 1m 4s
160:	learn: 0.0535763	total: 12.4s	remaining: 1m 4s
161:	learn: 0.0535168	total: 12.4s	remaining: 1m 4s
162:	learn: 0.0534676	total: 12.5s	remaining: 1m 4s
163:	learn: 0.0534103	total: 12.6s	remaining: 1m 4s
164:	learn: 0.0533377	total: 12.7s	remaining: 1m 4s
165:	learn: 0.0532860	total: 12.7s	remaining: 1m 4s
166:	learn: 0.0532360	total: 12.8s	remaining: 1m 4s
167:	learn: 0.0531490	total: 12.9s	remaining: 1m 4s
168:	learn: 0.0531317	total: 13s	remaining: 1m 3s
169:	learn: 0.0531092	total: 13.1s	remaining: 1m 3s
170:	learn: 0.0530680	total: 13.1s	remaining: 1m 3s
171:	learn: 0.0530472	total: 13.2s	remaining: 1m 3s
172:	learn: 0.0530063	total: 13.3s	remaining: 1m 3s
173:	learn: 0.0529521	total: 13.4s	remaining: 1m 3s
174:	learn: 0.0529113	total: 13.4s	remaining: 1m 3s
175:	learn: 0.0528566	total: 13.5s	remaining: 1m 3s
176:	learn: 0.0528088	total: 13.6s	remaining: 1m 3s
177:	learn: 0.0527460	total: 13.7s	remaining: 1m 3s
178:	learn: 0.0527284	total: 13.7s	remaining: 1m 2s
179:	learn: 0.0526977	total: 13.8s	remaining: 1m 2s
180:	learn: 0.0526645	total: 13.9s	remaining: 1m 2s
181:	learn: 0.0526053	total: 13.9s	remaining: 1m 2s
182:	learn: 0.0525473	total: 14s	remaining: 1m 2s
183:	learn: 0.0525328	total: 14.1s	remaining: 1m 2s
184:	learn: 0.0524945	total: 14.2s	remaining: 1m 2s
185:	learn: 0.0524246	total: 14.2s	remaining: 1m 2s
186:	learn: 0.0523550	total: 14.3s	remaining: 1m 2s
187:	learn: 0.0522987	total: 14.4s	remaining: 1m 2s
188:	learn: 0.0522551	total: 14.4s	remaining: 1m 1s
189:	learn: 0.0522374	total: 14.5s	remaining: 1m 1s
190:	learn: 0.0522126	total: 14.6s	remaining: 1m 1s
191:	learn: 0.0521781	total: 14.7s	remaining: 1m 1s
192:	learn: 0.0521401	total: 14.7s	remaining: 1m 1s
193:	learn: 0.0521106	total: 14.8s	remaining: 1m 1s
194:	learn: 0.0520531	total: 14.9s	remaining: 1m 1s
195:	learn: 0.0520134	total: 15s	remaining: 1m 1s
196:	learn: 0.0519875	total: 15s	remaining: 1m 1s
197:	learn: 0.0519543	total: 15.1s	remaining: 1m 1s
198:	learn: 0.0518962	total: 15.1s	remaining: 1m
199:	learn: 0.0518513	total: 15.2s	remaining: 1m
200:	learn: 0.0518092	total: 15.3s	remaining: 1m
201:	learn: 0.0517478	total: 15.4s	remaining: 1m
202:	learn: 0.0517223	total: 15.5s	remaining: 1m
203:	learn: 0.0516957	total: 15.5s	remaining: 1m
204:	learn: 0.0516686	total: 15.6s	remaining: 1m
205:	learn: 0.0516140	total: 15.6s	remaining: 1m
206:	learn: 0.0516110	total: 15.7s	remaining: 1m
207:	learn: 0.0515779	total: 15.8s	remaining: 1m
208:	learn: 0.0515643	total: 15.9s	remaining: 1m
209:	learn: 0.0515214	total: 15.9s	remaining: 60s
210:	learn: 0.0514887	total: 16s	remaining: 59.8s
211:	learn: 0.0514662	total: 16.1s	remaining: 59.7s
212:	learn: 0.0514439	total: 16.1s	remaining: 59.5s
213:	learn: 0.0514145	total: 16.2s	remaining: 59.4s
214:	learn: 0.0513843	total: 16.2s	remaining: 59.3s
215:	learn: 0.0513585	total: 16.3s	remaining: 59.2s
216:	learn: 0.0512504	total: 16.4s	remaining: 59.1s
217:	learn: 0.0511892	total: 16.5s	remaining: 59.1s
218:	learn: 0.0511454	total: 16.5s	remaining: 58.9s
219:	learn: 0.0511163	total: 16.6s	remaining: 58.9s
220:	learn: 0.0510796	total: 16.7s	remaining: 58.7s
221:	learn: 0.0510537	total: 16.7s	remaining: 58.6s
222:	learn: 0.0510135	total: 16.8s	remaining: 58.5s
223:	learn: 0.0509185	total: 16.9s	remaining: 58.5s
224:	learn: 0.0509007	total: 17s	remaining: 58.4s
225:	learn: 0.0508657	total: 17s	remaining: 58.3s
226:	learn: 0.0507716	total: 17.1s	remaining: 58.3s
227:	learn: 0.0507186	total: 17.2s	remaining: 58.2s
228:	learn: 0.0507042	total: 17.3s	remaining: 58.1s
229:	learn: 0.0506568	total: 17.3s	remaining: 58s
230:	learn: 0.0506353	total: 17.4s	remaining: 57.9s
231:	learn: 0.0506211	total: 17.4s	remaining: 57.7s
232:	learn: 0.0506186	total: 17.5s	remaining: 57.6s
233:	learn: 0.0505411	total: 17.6s	remaining: 57.6s
234:	learn: 0.0504964	total: 17.7s	remaining: 57.5s
235:	learn: 0.0504477	total: 17.7s	remaining: 57.4s
236:	learn: 0.0503699	total: 17.8s	remaining: 57.3s
237:	learn: 0.0503177	total: 17.9s	remaining: 57.2s
238:	learn: 0.0502705	total: 17.9s	remaining: 57.1s
239:	learn: 0.0502622	total: 18s	remaining: 57s
240:	learn: 0.0502303	total: 18.1s	remaining: 56.9s
241:	learn: 0.0502083	total: 18.1s	remaining: 56.8s
242:	learn: 0.0501758	total: 18.2s	remaining: 56.7s
243:	learn: 0.0501292	total: 18.3s	remaining: 56.6s
244:	learn: 0.0501018	total: 18.3s	remaining: 56.5s
245:	learn: 0.0500665	total: 18.4s	remaining: 56.4s
246:	learn: 0.0500173	total: 18.5s	remaining: 56.3s
247:	learn: 0.0500125	total: 18.5s	remaining: 56.2s
248:	learn: 0.0499729	total: 18.6s	remaining: 56.1s
249:	learn: 0.0499285	total: 18.7s	remaining: 56s
250:	learn: 0.0499226	total: 18.8s	remaining: 56s
251:	learn: 0.0498500	total: 18.8s	remaining: 55.9s
252:	learn: 0.0498284	total: 18.9s	remaining: 55.8s
253:	learn: 0.0497593	total: 19s	remaining: 55.7s
254:	learn: 0.0497556	total: 19s	remaining: 55.6s
255:	learn: 0.0497223	total: 19.1s	remaining: 55.5s
256:	learn: 0.0497045	total: 19.1s	remaining: 55.3s
257:	learn: 0.0496531	total: 19.2s	remaining: 55.3s
258:	learn: 0.0496084	total: 19.3s	remaining: 55.1s
259:	learn: 0.0496009	total: 19.3s	remaining: 55.1s
260:	learn: 0.0495191	total: 19.4s	remaining: 55s
261:	learn: 0.0494790	total: 19.5s	remaining: 54.9s
262:	learn: 0.0494432	total: 19.6s	remaining: 54.8s
263:	learn: 0.0494362	total: 19.6s	remaining: 54.7s
264:	learn: 0.0493463	total: 19.7s	remaining: 54.7s
265:	learn: 0.0492949	total: 19.8s	remaining: 54.6s
266:	learn: 0.0492222	total: 19.9s	remaining: 54.6s
267:	learn: 0.0491886	total: 19.9s	remaining: 54.5s
268:	learn: 0.0491477	total: 20s	remaining: 54.4s
269:	learn: 0.0490979	total: 20.1s	remaining: 54.3s
270:	learn: 0.0490506	total: 20.2s	remaining: 54.2s
271:	learn: 0.0490104	total: 20.2s	remaining: 54.1s
272:	learn: 0.0489466	total: 20.3s	remaining: 54s
273:	learn: 0.0489082	total: 20.4s	remaining: 53.9s
274:	learn: 0.0488504	total: 20.4s	remaining: 53.9s
275:	learn: 0.0488273	total: 20.5s	remaining: 53.8s
276:	learn: 0.0487831	total: 20.6s	remaining: 53.7s
277:	learn: 0.0487428	total: 20.6s	remaining: 53.6s
278:	learn: 0.0486984	total: 20.7s	remaining: 53.5s
279:	learn: 0.0486714	total: 20.8s	remaining: 53.4s
280:	learn: 0.0486577	total: 20.8s	remaining: 53.2s
281:	learn: 0.0485806	total: 20.9s	remaining: 53.2s
282:	learn: 0.0485517	total: 21s	remaining: 53.1s
283:	learn: 0.0485220	total: 21s	remaining: 53s
284:	learn: 0.0484923	total: 21.1s	remaining: 52.9s
285:	learn: 0.0484319	total: 21.2s	remaining: 52.8s
286:	learn: 0.0483889	total: 21.2s	remaining: 52.7s
287:	learn: 0.0483436	total: 21.3s	remaining: 52.6s
288:	learn: 0.0482777	total: 21.4s	remaining: 52.5s
289:	learn: 0.0482235	total: 21.4s	remaining: 52.5s
290:	learn: 0.0481588	total: 21.5s	remaining: 52.4s
291:	learn: 0.0481225	total: 21.6s	remaining: 52.3s
292:	learn: 0.0480879	total: 21.6s	remaining: 52.2s
293:	learn: 0.0480204	total: 21.7s	remaining: 52.1s
294:	learn: 0.0479618	total: 21.8s	remaining: 52s
295:	learn: 0.0479294	total: 21.8s	remaining: 51.9s
296:	learn: 0.0479008	total: 21.9s	remaining: 51.9s
297:	learn: 0.0478398	total: 22s	remaining: 51.8s
298:	learn: 0.0477826	total: 22.1s	remaining: 51.7s
299:	learn: 0.0477558	total: 22.1s	remaining: 51.6s
300:	learn: 0.0477484	total: 22.2s	remaining: 51.5s
301:	learn: 0.0477064	total: 22.2s	remaining: 51.4s
302:	learn: 0.0476099	total: 22.3s	remaining: 51.4s
303:	learn: 0.0475007	total: 22.4s	remaining: 51.3s
304:	learn: 0.0474824	total: 22.5s	remaining: 51.2s
305:	learn: 0.0474073	total: 22.5s	remaining: 51.1s
306:	learn: 0.0473760	total: 22.6s	remaining: 51s
307:	learn: 0.0473123	total: 22.7s	remaining: 51s
308:	learn: 0.0472584	total: 22.8s	remaining: 50.9s
309:	learn: 0.0471438	total: 22.8s	remaining: 50.8s
310:	learn: 0.0470912	total: 22.9s	remaining: 50.7s
311:	learn: 0.0470372	total: 23s	remaining: 50.7s
312:	learn: 0.0469938	total: 23.1s	remaining: 50.6s
313:	learn: 0.0469099	total: 23.1s	remaining: 50.6s
314:	learn: 0.0468545	total: 23.2s	remaining: 50.5s
315:	learn: 0.0468203	total: 23.3s	remaining: 50.4s
316:	learn: 0.0467614	total: 23.3s	remaining: 50.3s
317:	learn: 0.0467204	total: 23.4s	remaining: 50.2s
318:	learn: 0.0466860	total: 23.5s	remaining: 50.1s
319:	learn: 0.0466462	total: 23.5s	remaining: 50s
320:	learn: 0.0465574	total: 23.6s	remaining: 50s
321:	learn: 0.0464777	total: 23.7s	remaining: 49.9s
322:	learn: 0.0464096	total: 23.8s	remaining: 49.8s
323:	learn: 0.0463872	total: 23.8s	remaining: 49.7s
324:	learn: 0.0463123	total: 23.9s	remaining: 49.7s
325:	learn: 0.0462706	total: 24s	remaining: 49.6s
326:	learn: 0.0462401	total: 24.1s	remaining: 49.5s
327:	learn: 0.0461914	total: 24.1s	remaining: 49.5s
328:	learn: 0.0461424	total: 24.2s	remaining: 49.4s
329:	learn: 0.0460859	total: 24.3s	remaining: 49.3s
330:	learn: 0.0460528	total: 24.4s	remaining: 49.2s
331:	learn: 0.0460194	total: 24.4s	remaining: 49.1s
332:	learn: 0.0460090	total: 24.5s	remaining: 49.1s
333:	learn: 0.0459516	total: 24.6s	remaining: 49s
334:	learn: 0.0458873	total: 24.6s	remaining: 48.9s
335:	learn: 0.0458760	total: 24.7s	remaining: 48.8s
336:	learn: 0.0458541	total: 24.8s	remaining: 48.7s
337:	learn: 0.0457979	total: 24.8s	remaining: 48.6s
338:	learn: 0.0457781	total: 24.9s	remaining: 48.5s
339:	learn: 0.0457337	total: 25s	remaining: 48.4s
340:	learn: 0.0456939	total: 25s	remaining: 48.4s
341:	learn: 0.0456852	total: 25.1s	remaining: 48.3s
342:	learn: 0.0456445	total: 25.2s	remaining: 48.2s
343:	learn: 0.0455671	total: 25.3s	remaining: 48.2s
344:	learn: 0.0454937	total: 25.3s	remaining: 48.1s
345:	learn: 0.0454480	total: 25.4s	remaining: 48s
346:	learn: 0.0453786	total: 25.5s	remaining: 47.9s
347:	learn: 0.0453273	total: 25.5s	remaining: 47.9s
348:	learn: 0.0452289	total: 25.6s	remaining: 47.8s
349:	learn: 0.0452147	total: 25.7s	remaining: 47.7s
350:	learn: 0.0452018	total: 25.8s	remaining: 47.7s
351:	learn: 0.0451403	total: 25.9s	remaining: 47.6s
352:	learn: 0.0451071	total: 25.9s	remaining: 47.5s
353:	learn: 0.0450634	total: 26s	remaining: 47.4s
354:	learn: 0.0449842	total: 26.1s	remaining: 47.4s
355:	learn: 0.0449459	total: 26.1s	remaining: 47.3s
356:	learn: 0.0449006	total: 26.2s	remaining: 47.3s
357:	learn: 0.0448597	total: 26.3s	remaining: 47.2s
358:	learn: 0.0447865	total: 26.4s	remaining: 47.1s
359:	learn: 0.0447416	total: 26.5s	remaining: 47s
360:	learn: 0.0447297	total: 26.5s	remaining: 47s
361:	learn: 0.0446912	total: 26.6s	remaining: 46.9s
362:	learn: 0.0446480	total: 26.7s	remaining: 46.8s
363:	learn: 0.0446207	total: 26.7s	remaining: 46.7s
364:	learn: 0.0446075	total: 26.8s	remaining: 46.6s
365:	learn: 0.0445675	total: 26.9s	remaining: 46.5s
366:	learn: 0.0445427	total: 26.9s	remaining: 46.4s
367:	learn: 0.0444665	total: 27s	remaining: 46.4s
368:	learn: 0.0444177	total: 27.1s	remaining: 46.3s
369:	learn: 0.0443913	total: 27.1s	remaining: 46.2s
370:	learn: 0.0443416	total: 27.2s	remaining: 46.2s
371:	learn: 0.0442832	total: 27.3s	remaining: 46.1s
372:	learn: 0.0442235	total: 27.4s	remaining: 46s
373:	learn: 0.0441696	total: 27.4s	remaining: 45.9s
374:	learn: 0.0441266	total: 27.5s	remaining: 45.8s
375:	learn: 0.0440703	total: 27.6s	remaining: 45.8s
376:	learn: 0.0440175	total: 27.7s	remaining: 45.7s
377:	learn: 0.0439962	total: 27.7s	remaining: 45.6s
378:	learn: 0.0439546	total: 27.8s	remaining: 45.5s
379:	learn: 0.0439183	total: 27.9s	remaining: 45.5s
380:	learn: 0.0438771	total: 27.9s	remaining: 45.4s
381:	learn: 0.0438370	total: 28s	remaining: 45.3s
382:	learn: 0.0438120	total: 28.1s	remaining: 45.2s
383:	learn: 0.0437754	total: 28.1s	remaining: 45.1s
384:	learn: 0.0436981	total: 28.2s	remaining: 45.1s
385:	learn: 0.0436718	total: 28.3s	remaining: 45s
386:	learn: 0.0436593	total: 28.3s	remaining: 44.9s
387:	learn: 0.0436249	total: 28.4s	remaining: 44.8s
388:	learn: 0.0436064	total: 28.5s	remaining: 44.7s
389:	learn: 0.0435574	total: 28.5s	remaining: 44.6s
390:	learn: 0.0434781	total: 28.6s	remaining: 44.6s
391:	learn: 0.0434384	total: 28.7s	remaining: 44.5s
392:	learn: 0.0434145	total: 28.8s	remaining: 44.4s
393:	learn: 0.0433393	total: 28.8s	remaining: 44.4s
394:	learn: 0.0432971	total: 28.9s	remaining: 44.3s
395:	learn: 0.0432718	total: 29s	remaining: 44.2s
396:	learn: 0.0432250	total: 29s	remaining: 44.1s
397:	learn: 0.0431619	total: 29.1s	remaining: 44s
398:	learn: 0.0431345	total: 29.2s	remaining: 44s
399:	learn: 0.0430763	total: 29.2s	remaining: 43.9s
400:	learn: 0.0429876	total: 29.3s	remaining: 43.8s
401:	learn: 0.0429262	total: 29.4s	remaining: 43.8s
402:	learn: 0.0428875	total: 29.5s	remaining: 43.7s
403:	learn: 0.0428368	total: 29.5s	remaining: 43.6s
404:	learn: 0.0427507	total: 29.6s	remaining: 43.5s
405:	learn: 0.0426994	total: 29.7s	remaining: 43.4s
406:	learn: 0.0426674	total: 29.8s	remaining: 43.4s
407:	learn: 0.0426270	total: 29.8s	remaining: 43.3s
408:	learn: 0.0425918	total: 29.9s	remaining: 43.2s
409:	learn: 0.0425748	total: 30s	remaining: 43.1s
410:	learn: 0.0424962	total: 30s	remaining: 43.1s
411:	learn: 0.0424475	total: 30.1s	remaining: 43s
412:	learn: 0.0424296	total: 30.2s	remaining: 42.9s
413:	learn: 0.0424178	total: 30.2s	remaining: 42.8s
414:	learn: 0.0423509	total: 30.3s	remaining: 42.7s
415:	learn: 0.0422971	total: 30.4s	remaining: 42.7s
416:	learn: 0.0422592	total: 30.5s	remaining: 42.6s
417:	learn: 0.0422204	total: 30.5s	remaining: 42.5s
418:	learn: 0.0421705	total: 30.6s	remaining: 42.5s
419:	learn: 0.0421530	total: 30.7s	remaining: 42.4s
420:	learn: 0.0421341	total: 30.8s	remaining: 42.3s
421:	learn: 0.0421124	total: 30.8s	remaining: 42.2s
422:	learn: 0.0420818	total: 30.9s	remaining: 42.1s
423:	learn: 0.0420356	total: 31s	remaining: 42.1s
424:	learn: 0.0420019	total: 31s	remaining: 42s
425:	learn: 0.0419519	total: 31.1s	remaining: 41.9s
426:	learn: 0.0419162	total: 31.2s	remaining: 41.8s
427:	learn: 0.0418522	total: 31.3s	remaining: 41.8s
428:	learn: 0.0418053	total: 31.3s	remaining: 41.7s
429:	learn: 0.0417471	total: 31.4s	remaining: 41.6s
430:	learn: 0.0416888	total: 31.5s	remaining: 41.5s
431:	learn: 0.0416621	total: 31.5s	remaining: 41.5s
432:	learn: 0.0416314	total: 31.6s	remaining: 41.4s
433:	learn: 0.0416142	total: 31.7s	remaining: 41.3s
434:	learn: 0.0415544	total: 31.8s	remaining: 41.2s
435:	learn: 0.0415436	total: 31.8s	remaining: 41.2s
436:	learn: 0.0414751	total: 31.9s	remaining: 41.1s
437:	learn: 0.0414670	total: 32s	remaining: 41s
438:	learn: 0.0414293	total: 32s	remaining: 40.9s
439:	learn: 0.0413700	total: 32.1s	remaining: 40.8s
440:	learn: 0.0413030	total: 32.2s	remaining: 40.8s
441:	learn: 0.0412940	total: 32.2s	remaining: 40.7s
442:	learn: 0.0412788	total: 32.3s	remaining: 40.6s
443:	learn: 0.0412226	total: 32.4s	remaining: 40.5s
444:	learn: 0.0411663	total: 32.4s	remaining: 40.5s
445:	learn: 0.0411548	total: 32.5s	remaining: 40.4s
446:	learn: 0.0411448	total: 32.6s	remaining: 40.3s
447:	learn: 0.0410926	total: 32.6s	remaining: 40.2s
448:	learn: 0.0410850	total: 32.7s	remaining: 40.1s
449:	learn: 0.0410478	total: 32.8s	remaining: 40s
450:	learn: 0.0409744	total: 32.8s	remaining: 40s
451:	learn: 0.0409672	total: 32.9s	remaining: 39.9s
452:	learn: 0.0409190	total: 33s	remaining: 39.8s
453:	learn: 0.0408880	total: 33.1s	remaining: 39.8s
454:	learn: 0.0408808	total: 33.1s	remaining: 39.7s
455:	learn: 0.0408573	total: 33.2s	remaining: 39.6s
456:	learn: 0.0408352	total: 33.3s	remaining: 39.5s
457:	learn: 0.0407845	total: 33.4s	remaining: 39.5s
458:	learn: 0.0407697	total: 33.4s	remaining: 39.4s
459:	learn: 0.0407013	total: 33.5s	remaining: 39.3s
460:	learn: 0.0406817	total: 33.6s	remaining: 39.2s
461:	learn: 0.0406444	total: 33.6s	remaining: 39.2s
462:	learn: 0.0406224	total: 33.7s	remaining: 39.1s
463:	learn: 0.0406009	total: 33.8s	remaining: 39s
464:	learn: 0.0405504	total: 33.8s	remaining: 38.9s
465:	learn: 0.0405359	total: 33.9s	remaining: 38.8s
466:	learn: 0.0405042	total: 34s	remaining: 38.8s
467:	learn: 0.0404656	total: 34s	remaining: 38.7s
468:	learn: 0.0404018	total: 34.1s	remaining: 38.6s
469:	learn: 0.0403874	total: 34.2s	remaining: 38.6s
470:	learn: 0.0403866	total: 34.3s	remaining: 38.5s
471:	learn: 0.0403204	total: 34.3s	remaining: 38.4s
472:	learn: 0.0402996	total: 34.4s	remaining: 38.3s
473:	learn: 0.0402181	total: 34.5s	remaining: 38.3s
474:	learn: 0.0402098	total: 34.5s	remaining: 38.2s
475:	learn: 0.0401572	total: 34.6s	remaining: 38.1s
476:	learn: 0.0401252	total: 34.7s	remaining: 38s
477:	learn: 0.0400788	total: 34.8s	remaining: 38s
478:	learn: 0.0400767	total: 34.8s	remaining: 37.9s
479:	learn: 0.0399963	total: 34.9s	remaining: 37.8s
480:	learn: 0.0399785	total: 35s	remaining: 37.7s
481:	learn: 0.0399359	total: 35s	remaining: 37.7s
482:	learn: 0.0398898	total: 35.1s	remaining: 37.6s
483:	learn: 0.0398537	total: 35.2s	remaining: 37.5s
484:	learn: 0.0397978	total: 35.3s	remaining: 37.5s
485:	learn: 0.0397681	total: 35.4s	remaining: 37.4s
486:	learn: 0.0396638	total: 35.4s	remaining: 37.3s
487:	learn: 0.0396477	total: 35.5s	remaining: 37.3s
488:	learn: 0.0396404	total: 35.6s	remaining: 37.2s
489:	learn: 0.0396331	total: 35.6s	remaining: 37.1s
490:	learn: 0.0395818	total: 35.7s	remaining: 37s
491:	learn: 0.0395797	total: 35.8s	remaining: 36.9s
492:	learn: 0.0395441	total: 35.8s	remaining: 36.9s
493:	learn: 0.0394972	total: 35.9s	remaining: 36.8s
494:	learn: 0.0394555	total: 36s	remaining: 36.7s
495:	learn: 0.0394063	total: 36.1s	remaining: 36.6s
496:	learn: 0.0393509	total: 36.1s	remaining: 36.6s
497:	learn: 0.0392747	total: 36.2s	remaining: 36.5s
498:	learn: 0.0392167	total: 36.3s	remaining: 36.5s
499:	learn: 0.0391588	total: 36.4s	remaining: 36.4s
500:	learn: 0.0390962	total: 36.5s	remaining: 36.3s
501:	learn: 0.0390407	total: 36.6s	remaining: 36.3s
502:	learn: 0.0389865	total: 36.7s	remaining: 36.2s
503:	learn: 0.0389717	total: 36.7s	remaining: 36.1s
504:	learn: 0.0389122	total: 36.8s	remaining: 36.1s
505:	learn: 0.0388483	total: 36.9s	remaining: 36s
506:	learn: 0.0388274	total: 36.9s	remaining: 35.9s
507:	learn: 0.0387777	total: 37s	remaining: 35.9s
508:	learn: 0.0387355	total: 37.1s	remaining: 35.8s
509:	learn: 0.0387024	total: 37.2s	remaining: 35.7s
510:	learn: 0.0386646	total: 37.2s	remaining: 35.6s
511:	learn: 0.0386193	total: 37.3s	remaining: 35.6s
512:	learn: 0.0385870	total: 37.4s	remaining: 35.5s
513:	learn: 0.0385494	total: 37.5s	remaining: 35.4s
514:	learn: 0.0384983	total: 37.6s	remaining: 35.4s
515:	learn: 0.0384402	total: 37.6s	remaining: 35.3s
516:	learn: 0.0384154	total: 37.7s	remaining: 35.2s
517:	learn: 0.0383608	total: 37.8s	remaining: 35.1s
518:	learn: 0.0383128	total: 37.8s	remaining: 35.1s
519:	learn: 0.0383069	total: 37.9s	remaining: 35s
520:	learn: 0.0382834	total: 38s	remaining: 34.9s
521:	learn: 0.0382324	total: 38.1s	remaining: 34.9s
522:	learn: 0.0382174	total: 38.1s	remaining: 34.8s
523:	learn: 0.0382086	total: 38.2s	remaining: 34.7s
524:	learn: 0.0381881	total: 38.3s	remaining: 34.6s
525:	learn: 0.0381276	total: 38.3s	remaining: 34.5s
526:	learn: 0.0381019	total: 38.4s	remaining: 34.5s
527:	learn: 0.0380768	total: 38.5s	remaining: 34.4s
528:	learn: 0.0380436	total: 38.5s	remaining: 34.3s
529:	learn: 0.0379974	total: 38.6s	remaining: 34.2s
530:	learn: 0.0379122	total: 38.7s	remaining: 34.2s
531:	learn: 0.0378641	total: 38.8s	remaining: 34.1s
532:	learn: 0.0378239	total: 38.9s	remaining: 34s
533:	learn: 0.0377858	total: 38.9s	remaining: 34s
534:	learn: 0.0377559	total: 39s	remaining: 33.9s
535:	learn: 0.0377223	total: 39.1s	remaining: 33.8s
536:	learn: 0.0376742	total: 39.1s	remaining: 33.7s
537:	learn: 0.0375971	total: 39.2s	remaining: 33.7s
538:	learn: 0.0375671	total: 39.3s	remaining: 33.6s
539:	learn: 0.0375524	total: 39.4s	remaining: 33.5s
540:	learn: 0.0375394	total: 39.4s	remaining: 33.4s
541:	learn: 0.0375306	total: 39.5s	remaining: 33.4s
542:	learn: 0.0374970	total: 39.5s	remaining: 33.3s
543:	learn: 0.0374581	total: 39.6s	remaining: 33.2s
544:	learn: 0.0374218	total: 39.7s	remaining: 33.1s
545:	learn: 0.0373873	total: 39.8s	remaining: 33.1s
546:	learn: 0.0373320	total: 39.8s	remaining: 33s
547:	learn: 0.0373022	total: 39.9s	remaining: 32.9s
548:	learn: 0.0372286	total: 40s	remaining: 32.8s
549:	learn: 0.0371892	total: 40s	remaining: 32.8s
550:	learn: 0.0371465	total: 40.1s	remaining: 32.7s
551:	learn: 0.0371290	total: 40.2s	remaining: 32.6s
552:	learn: 0.0370665	total: 40.2s	remaining: 32.5s
553:	learn: 0.0370403	total: 40.3s	remaining: 32.5s
554:	learn: 0.0370317	total: 40.4s	remaining: 32.4s
555:	learn: 0.0369992	total: 40.5s	remaining: 32.3s
556:	learn: 0.0369931	total: 40.5s	remaining: 32.2s
557:	learn: 0.0369659	total: 40.6s	remaining: 32.1s
558:	learn: 0.0369291	total: 40.6s	remaining: 32.1s
559:	learn: 0.0369097	total: 40.7s	remaining: 32s
560:	learn: 0.0368887	total: 40.8s	remaining: 31.9s
561:	learn: 0.0368412	total: 40.8s	remaining: 31.8s
562:	learn: 0.0368030	total: 40.9s	remaining: 31.8s
563:	learn: 0.0367914	total: 41s	remaining: 31.7s
564:	learn: 0.0367387	total: 41.1s	remaining: 31.6s
565:	learn: 0.0367320	total: 41.1s	remaining: 31.5s
566:	learn: 0.0366717	total: 41.2s	remaining: 31.5s
567:	learn: 0.0366348	total: 41.3s	remaining: 31.4s
568:	learn: 0.0366184	total: 41.4s	remaining: 31.3s
569:	learn: 0.0365705	total: 41.4s	remaining: 31.3s
570:	learn: 0.0365279	total: 41.5s	remaining: 31.2s
571:	learn: 0.0364734	total: 41.6s	remaining: 31.1s
572:	learn: 0.0364662	total: 41.6s	remaining: 31s
573:	learn: 0.0364555	total: 41.7s	remaining: 30.9s
574:	learn: 0.0364378	total: 41.8s	remaining: 30.9s
575:	learn: 0.0364166	total: 41.8s	remaining: 30.8s
576:	learn: 0.0363693	total: 41.9s	remaining: 30.7s
577:	learn: 0.0362999	total: 42s	remaining: 30.7s
578:	learn: 0.0362949	total: 42.1s	remaining: 30.6s
579:	learn: 0.0362714	total: 42.1s	remaining: 30.5s
580:	learn: 0.0362424	total: 42.2s	remaining: 30.4s
581:	learn: 0.0362011	total: 42.3s	remaining: 30.4s
582:	learn: 0.0361785	total: 42.4s	remaining: 30.3s
583:	learn: 0.0361270	total: 42.5s	remaining: 30.2s
584:	learn: 0.0361053	total: 42.5s	remaining: 30.2s
585:	learn: 0.0360445	total: 42.6s	remaining: 30.1s
586:	learn: 0.0359987	total: 42.7s	remaining: 30s
587:	learn: 0.0359735	total: 42.7s	remaining: 30s
588:	learn: 0.0359435	total: 42.8s	remaining: 29.9s
589:	learn: 0.0359002	total: 42.9s	remaining: 29.8s
590:	learn: 0.0358801	total: 43s	remaining: 29.7s
591:	learn: 0.0358208	total: 43s	remaining: 29.7s
592:	learn: 0.0357533	total: 43.1s	remaining: 29.6s
593:	learn: 0.0357449	total: 43.2s	remaining: 29.5s
594:	learn: 0.0357359	total: 43.2s	remaining: 29.4s
595:	learn: 0.0357107	total: 43.3s	remaining: 29.4s
596:	learn: 0.0356811	total: 43.4s	remaining: 29.3s
597:	learn: 0.0356169	total: 43.5s	remaining: 29.2s
598:	learn: 0.0355569	total: 43.6s	remaining: 29.2s
599:	learn: 0.0354719	total: 43.7s	remaining: 29.1s
600:	learn: 0.0354228	total: 43.7s	remaining: 29s
601:	learn: 0.0354002	total: 43.8s	remaining: 29s
602:	learn: 0.0353745	total: 43.9s	remaining: 28.9s
603:	learn: 0.0353274	total: 43.9s	remaining: 28.8s
604:	learn: 0.0352682	total: 44s	remaining: 28.7s
605:	learn: 0.0352117	total: 44.1s	remaining: 28.7s
606:	learn: 0.0351985	total: 44.2s	remaining: 28.6s
607:	learn: 0.0351733	total: 44.2s	remaining: 28.5s
608:	learn: 0.0351071	total: 44.3s	remaining: 28.5s
609:	learn: 0.0350470	total: 44.4s	remaining: 28.4s
610:	learn: 0.0350190	total: 44.5s	remaining: 28.3s
611:	learn: 0.0349856	total: 44.6s	remaining: 28.3s
612:	learn: 0.0349800	total: 44.6s	remaining: 28.2s
613:	learn: 0.0349439	total: 44.7s	remaining: 28.1s
614:	learn: 0.0349200	total: 44.8s	remaining: 28s
615:	learn: 0.0348596	total: 44.8s	remaining: 28s
616:	learn: 0.0348293	total: 44.9s	remaining: 27.9s
617:	learn: 0.0347724	total: 45s	remaining: 27.8s
618:	learn: 0.0347448	total: 45.1s	remaining: 27.7s
619:	learn: 0.0347365	total: 45.1s	remaining: 27.7s
620:	learn: 0.0347067	total: 45.2s	remaining: 27.6s
621:	learn: 0.0346730	total: 45.3s	remaining: 27.5s
622:	learn: 0.0346540	total: 45.3s	remaining: 27.4s
623:	learn: 0.0346119	total: 45.4s	remaining: 27.4s
624:	learn: 0.0345704	total: 45.5s	remaining: 27.3s
625:	learn: 0.0345295	total: 45.6s	remaining: 27.2s
626:	learn: 0.0345029	total: 45.6s	remaining: 27.2s
627:	learn: 0.0344716	total: 45.7s	remaining: 27.1s
628:	learn: 0.0344189	total: 45.8s	remaining: 27s
629:	learn: 0.0343639	total: 45.9s	remaining: 26.9s
630:	learn: 0.0343130	total: 46s	remaining: 26.9s
631:	learn: 0.0342333	total: 46.1s	remaining: 26.8s
632:	learn: 0.0341638	total: 46.1s	remaining: 26.7s
633:	learn: 0.0341373	total: 46.2s	remaining: 26.7s
634:	learn: 0.0341243	total: 46.3s	remaining: 26.6s
635:	learn: 0.0340834	total: 46.3s	remaining: 26.5s
636:	learn: 0.0340687	total: 46.4s	remaining: 26.4s
637:	learn: 0.0340380	total: 46.5s	remaining: 26.4s
638:	learn: 0.0339913	total: 46.5s	remaining: 26.3s
639:	learn: 0.0339325	total: 46.6s	remaining: 26.2s
640:	learn: 0.0338894	total: 46.7s	remaining: 26.2s
641:	learn: 0.0338784	total: 46.8s	remaining: 26.1s
642:	learn: 0.0338370	total: 46.9s	remaining: 26s
643:	learn: 0.0338220	total: 46.9s	remaining: 25.9s
644:	learn: 0.0337590	total: 47s	remaining: 25.9s
645:	learn: 0.0337295	total: 47.1s	remaining: 25.8s
646:	learn: 0.0337127	total: 47.1s	remaining: 25.7s
647:	learn: 0.0336682	total: 47.2s	remaining: 25.6s
648:	learn: 0.0336228	total: 47.3s	remaining: 25.6s
649:	learn: 0.0335796	total: 47.4s	remaining: 25.5s
650:	learn: 0.0335423	total: 47.4s	remaining: 25.4s
651:	learn: 0.0334907	total: 47.5s	remaining: 25.4s
652:	learn: 0.0334694	total: 47.6s	remaining: 25.3s
653:	learn: 0.0334292	total: 47.6s	remaining: 25.2s
654:	learn: 0.0334221	total: 47.7s	remaining: 25.1s
655:	learn: 0.0333955	total: 47.8s	remaining: 25.1s
656:	learn: 0.0333907	total: 47.9s	remaining: 25s
657:	learn: 0.0333713	total: 47.9s	remaining: 24.9s
658:	learn: 0.0333324	total: 48s	remaining: 24.8s
659:	learn: 0.0333134	total: 48s	remaining: 24.7s
660:	learn: 0.0332940	total: 48.1s	remaining: 24.7s
661:	learn: 0.0332301	total: 48.2s	remaining: 24.6s
662:	learn: 0.0332039	total: 48.3s	remaining: 24.5s
663:	learn: 0.0331709	total: 48.3s	remaining: 24.5s
664:	learn: 0.0331656	total: 48.4s	remaining: 24.4s
665:	learn: 0.0331350	total: 48.5s	remaining: 24.3s
666:	learn: 0.0330828	total: 48.6s	remaining: 24.2s
667:	learn: 0.0330482	total: 48.6s	remaining: 24.2s
668:	learn: 0.0330399	total: 48.7s	remaining: 24.1s
669:	learn: 0.0330348	total: 48.8s	remaining: 24s
670:	learn: 0.0330007	total: 48.8s	remaining: 23.9s
671:	learn: 0.0329924	total: 48.9s	remaining: 23.9s
672:	learn: 0.0329842	total: 49s	remaining: 23.8s
673:	learn: 0.0329146	total: 49s	remaining: 23.7s
674:	learn: 0.0328830	total: 49.1s	remaining: 23.6s
675:	learn: 0.0328588	total: 49.2s	remaining: 23.6s
676:	learn: 0.0328136	total: 49.3s	remaining: 23.5s
677:	learn: 0.0327709	total: 49.3s	remaining: 23.4s
678:	learn: 0.0327502	total: 49.4s	remaining: 23.3s
679:	learn: 0.0327143	total: 49.5s	remaining: 23.3s
680:	learn: 0.0326987	total: 49.5s	remaining: 23.2s
681:	learn: 0.0326870	total: 49.6s	remaining: 23.1s
682:	learn: 0.0326780	total: 49.7s	remaining: 23s
683:	learn: 0.0326663	total: 49.7s	remaining: 23s
684:	learn: 0.0326306	total: 49.8s	remaining: 22.9s
685:	learn: 0.0325924	total: 49.9s	remaining: 22.8s
686:	learn: 0.0325621	total: 49.9s	remaining: 22.7s
687:	learn: 0.0325341	total: 50s	remaining: 22.7s
688:	learn: 0.0325050	total: 50.1s	remaining: 22.6s
689:	learn: 0.0324947	total: 50.1s	remaining: 22.5s
690:	learn: 0.0324912	total: 50.2s	remaining: 22.4s
691:	learn: 0.0324431	total: 50.3s	remaining: 22.4s
692:	learn: 0.0323906	total: 50.4s	remaining: 22.3s
693:	learn: 0.0323281	total: 50.5s	remaining: 22.2s
694:	learn: 0.0322881	total: 50.5s	remaining: 22.2s
695:	learn: 0.0322395	total: 50.6s	remaining: 22.1s
696:	learn: 0.0322218	total: 50.7s	remaining: 22s
697:	learn: 0.0322008	total: 50.8s	remaining: 22s
698:	learn: 0.0321753	total: 50.8s	remaining: 21.9s
699:	learn: 0.0321590	total: 50.9s	remaining: 21.8s
700:	learn: 0.0321407	total: 51s	remaining: 21.7s
701:	learn: 0.0321029	total: 51s	remaining: 21.7s
702:	learn: 0.0320545	total: 51.1s	remaining: 21.6s
703:	learn: 0.0320275	total: 51.2s	remaining: 21.5s
704:	learn: 0.0319942	total: 51.3s	remaining: 21.5s
705:	learn: 0.0319904	total: 51.3s	remaining: 21.4s
706:	learn: 0.0319462	total: 51.4s	remaining: 21.3s
707:	learn: 0.0318977	total: 51.5s	remaining: 21.2s
708:	learn: 0.0318922	total: 51.6s	remaining: 21.2s
709:	learn: 0.0318780	total: 51.6s	remaining: 21.1s
710:	learn: 0.0318655	total: 51.7s	remaining: 21s
711:	learn: 0.0318284	total: 51.8s	remaining: 20.9s
712:	learn: 0.0317875	total: 51.8s	remaining: 20.9s
713:	learn: 0.0317551	total: 51.9s	remaining: 20.8s
714:	learn: 0.0317057	total: 52s	remaining: 20.7s
715:	learn: 0.0316600	total: 52.1s	remaining: 20.7s
716:	learn: 0.0316503	total: 52.1s	remaining: 20.6s
717:	learn: 0.0316293	total: 52.2s	remaining: 20.5s
718:	learn: 0.0316243	total: 52.3s	remaining: 20.4s
719:	learn: 0.0315934	total: 52.3s	remaining: 20.4s
720:	learn: 0.0315608	total: 52.4s	remaining: 20.3s
721:	learn: 0.0315059	total: 52.5s	remaining: 20.2s
722:	learn: 0.0314504	total: 52.6s	remaining: 20.2s
723:	learn: 0.0314118	total: 52.7s	remaining: 20.1s
724:	learn: 0.0313780	total: 52.8s	remaining: 20s
725:	learn: 0.0313700	total: 52.8s	remaining: 19.9s
726:	learn: 0.0313247	total: 52.9s	remaining: 19.9s
727:	learn: 0.0312809	total: 53s	remaining: 19.8s
728:	learn: 0.0312679	total: 53.1s	remaining: 19.7s
729:	learn: 0.0312522	total: 53.1s	remaining: 19.7s
730:	learn: 0.0312236	total: 53.2s	remaining: 19.6s
731:	learn: 0.0312201	total: 53.3s	remaining: 19.5s
732:	learn: 0.0311881	total: 53.4s	remaining: 19.4s
733:	learn: 0.0311390	total: 53.4s	remaining: 19.4s
734:	learn: 0.0311221	total: 53.5s	remaining: 19.3s
735:	learn: 0.0310661	total: 53.6s	remaining: 19.2s
736:	learn: 0.0309900	total: 53.7s	remaining: 19.2s
737:	learn: 0.0309467	total: 53.7s	remaining: 19.1s
738:	learn: 0.0309358	total: 53.8s	remaining: 19s
739:	learn: 0.0309154	total: 53.9s	remaining: 18.9s
740:	learn: 0.0308757	total: 54s	remaining: 18.9s
741:	learn: 0.0308593	total: 54s	remaining: 18.8s
742:	learn: 0.0308301	total: 54.1s	remaining: 18.7s
743:	learn: 0.0308149	total: 54.2s	remaining: 18.6s
744:	learn: 0.0308004	total: 54.2s	remaining: 18.6s
745:	learn: 0.0307548	total: 54.3s	remaining: 18.5s
746:	learn: 0.0307378	total: 54.4s	remaining: 18.4s
747:	learn: 0.0306805	total: 54.5s	remaining: 18.3s
748:	learn: 0.0306386	total: 54.6s	remaining: 18.3s
749:	learn: 0.0306075	total: 54.6s	remaining: 18.2s
750:	learn: 0.0305754	total: 54.7s	remaining: 18.1s
751:	learn: 0.0305460	total: 54.8s	remaining: 18.1s
752:	learn: 0.0305202	total: 54.9s	remaining: 18s
753:	learn: 0.0304830	total: 55s	remaining: 17.9s
754:	learn: 0.0304403	total: 55.1s	remaining: 17.9s
755:	learn: 0.0304323	total: 55.1s	remaining: 17.8s
756:	learn: 0.0303876	total: 55.2s	remaining: 17.7s
757:	learn: 0.0303529	total: 55.3s	remaining: 17.6s
758:	learn: 0.0303288	total: 55.3s	remaining: 17.6s
759:	learn: 0.0302633	total: 55.4s	remaining: 17.5s
760:	learn: 0.0302503	total: 55.5s	remaining: 17.4s
761:	learn: 0.0302151	total: 55.6s	remaining: 17.4s
762:	learn: 0.0301727	total: 55.7s	remaining: 17.3s
763:	learn: 0.0301252	total: 55.8s	remaining: 17.2s
764:	learn: 0.0301010	total: 55.8s	remaining: 17.2s
765:	learn: 0.0300871	total: 55.9s	remaining: 17.1s
766:	learn: 0.0300533	total: 56s	remaining: 17s
767:	learn: 0.0300343	total: 56s	remaining: 16.9s
768:	learn: 0.0299958	total: 56.1s	remaining: 16.9s
769:	learn: 0.0299848	total: 56.2s	remaining: 16.8s
770:	learn: 0.0299468	total: 56.3s	remaining: 16.7s
771:	learn: 0.0299047	total: 56.3s	remaining: 16.6s
772:	learn: 0.0298662	total: 56.4s	remaining: 16.6s
773:	learn: 0.0298457	total: 56.5s	remaining: 16.5s
774:	learn: 0.0298341	total: 56.5s	remaining: 16.4s
775:	learn: 0.0298173	total: 56.6s	remaining: 16.3s
776:	learn: 0.0298087	total: 56.7s	remaining: 16.3s
777:	learn: 0.0297860	total: 56.8s	remaining: 16.2s
778:	learn: 0.0297554	total: 56.9s	remaining: 16.1s
779:	learn: 0.0297334	total: 56.9s	remaining: 16.1s
780:	learn: 0.0297147	total: 57s	remaining: 16s
781:	learn: 0.0296795	total: 57.1s	remaining: 15.9s
782:	learn: 0.0296438	total: 57.2s	remaining: 15.8s
783:	learn: 0.0296356	total: 57.2s	remaining: 15.8s
784:	learn: 0.0295908	total: 57.3s	remaining: 15.7s
785:	learn: 0.0295639	total: 57.4s	remaining: 15.6s
786:	learn: 0.0295555	total: 57.5s	remaining: 15.5s
787:	learn: 0.0295177	total: 57.5s	remaining: 15.5s
788:	learn: 0.0294907	total: 57.6s	remaining: 15.4s
789:	learn: 0.0294814	total: 57.7s	remaining: 15.3s
790:	learn: 0.0294657	total: 57.8s	remaining: 15.3s
791:	learn: 0.0294194	total: 57.8s	remaining: 15.2s
792:	learn: 0.0294106	total: 57.9s	remaining: 15.1s
793:	learn: 0.0294069	total: 58s	remaining: 15s
794:	learn: 0.0293784	total: 58s	remaining: 15s
795:	learn: 0.0293343	total: 58.1s	remaining: 14.9s
796:	learn: 0.0292885	total: 58.2s	remaining: 14.8s
797:	learn: 0.0292501	total: 58.3s	remaining: 14.8s
798:	learn: 0.0292439	total: 58.4s	remaining: 14.7s
799:	learn: 0.0292236	total: 58.4s	remaining: 14.6s
800:	learn: 0.0292044	total: 58.5s	remaining: 14.5s
801:	learn: 0.0291747	total: 58.6s	remaining: 14.5s
802:	learn: 0.0291670	total: 58.6s	remaining: 14.4s
803:	learn: 0.0291563	total: 58.7s	remaining: 14.3s
804:	learn: 0.0291422	total: 58.8s	remaining: 14.2s
805:	learn: 0.0290968	total: 58.9s	remaining: 14.2s
806:	learn: 0.0290775	total: 58.9s	remaining: 14.1s
807:	learn: 0.0290375	total: 59s	remaining: 14s
808:	learn: 0.0290146	total: 59.1s	remaining: 14s
809:	learn: 0.0289875	total: 59.2s	remaining: 13.9s
810:	learn: 0.0289810	total: 59.3s	remaining: 13.8s
811:	learn: 0.0289748	total: 59.3s	remaining: 13.7s
812:	learn: 0.0289450	total: 59.4s	remaining: 13.7s
813:	learn: 0.0289022	total: 59.5s	remaining: 13.6s
814:	learn: 0.0288758	total: 59.5s	remaining: 13.5s
815:	learn: 0.0288313	total: 59.6s	remaining: 13.4s
816:	learn: 0.0287947	total: 59.7s	remaining: 13.4s
817:	learn: 0.0287884	total: 59.8s	remaining: 13.3s
818:	learn: 0.0287858	total: 59.8s	remaining: 13.2s
819:	learn: 0.0287604	total: 59.9s	remaining: 13.2s
820:	learn: 0.0287102	total: 60s	remaining: 13.1s
821:	learn: 0.0286960	total: 1m	remaining: 13s
822:	learn: 0.0286435	total: 1m	remaining: 12.9s
823:	learn: 0.0286132	total: 1m	remaining: 12.9s
824:	learn: 0.0286108	total: 1m	remaining: 12.8s
825:	learn: 0.0285866	total: 1m	remaining: 12.7s
826:	learn: 0.0285723	total: 1m	remaining: 12.6s
827:	learn: 0.0285649	total: 1m	remaining: 12.6s
828:	learn: 0.0285534	total: 1m	remaining: 12.5s
829:	learn: 0.0285252	total: 1m	remaining: 12.4s
830:	learn: 0.0285104	total: 1m	remaining: 12.3s
831:	learn: 0.0284769	total: 1m	remaining: 12.3s
832:	learn: 0.0284568	total: 1m	remaining: 12.2s
833:	learn: 0.0284247	total: 1m	remaining: 12.1s
834:	learn: 0.0284001	total: 1m 1s	remaining: 12.1s
835:	learn: 0.0283846	total: 1m 1s	remaining: 12s
836:	learn: 0.0283558	total: 1m 1s	remaining: 11.9s
837:	learn: 0.0282939	total: 1m 1s	remaining: 11.9s
838:	learn: 0.0282905	total: 1m 1s	remaining: 11.8s
839:	learn: 0.0282545	total: 1m 1s	remaining: 11.7s
840:	learn: 0.0282463	total: 1m 1s	remaining: 11.6s
841:	learn: 0.0282314	total: 1m 1s	remaining: 11.6s
842:	learn: 0.0282056	total: 1m 1s	remaining: 11.5s
843:	learn: 0.0281668	total: 1m 1s	remaining: 11.4s
844:	learn: 0.0281457	total: 1m 1s	remaining: 11.3s
845:	learn: 0.0281049	total: 1m 1s	remaining: 11.3s
846:	learn: 0.0280788	total: 1m 1s	remaining: 11.2s
847:	learn: 0.0280570	total: 1m 2s	remaining: 11.1s
848:	learn: 0.0280490	total: 1m 2s	remaining: 11s
849:	learn: 0.0280468	total: 1m 2s	remaining: 11s
850:	learn: 0.0280134	total: 1m 2s	remaining: 10.9s
851:	learn: 0.0279865	total: 1m 2s	remaining: 10.8s
852:	learn: 0.0279513	total: 1m 2s	remaining: 10.8s
853:	learn: 0.0279380	total: 1m 2s	remaining: 10.7s
854:	learn: 0.0279202	total: 1m 2s	remaining: 10.6s
855:	learn: 0.0278928	total: 1m 2s	remaining: 10.5s
856:	learn: 0.0278819	total: 1m 2s	remaining: 10.5s
857:	learn: 0.0278652	total: 1m 2s	remaining: 10.4s
858:	learn: 0.0278417	total: 1m 2s	remaining: 10.3s
859:	learn: 0.0278200	total: 1m 2s	remaining: 10.2s
860:	learn: 0.0277857	total: 1m 2s	remaining: 10.2s
861:	learn: 0.0277803	total: 1m 3s	remaining: 10.1s
862:	learn: 0.0277741	total: 1m 3s	remaining: 10s
863:	learn: 0.0277552	total: 1m 3s	remaining: 9.95s
864:	learn: 0.0277297	total: 1m 3s	remaining: 9.88s
865:	learn: 0.0276986	total: 1m 3s	remaining: 9.8s
866:	learn: 0.0276927	total: 1m 3s	remaining: 9.73s
867:	learn: 0.0276421	total: 1m 3s	remaining: 9.66s
868:	learn: 0.0276214	total: 1m 3s	remaining: 9.59s
869:	learn: 0.0276093	total: 1m 3s	remaining: 9.51s
870:	learn: 0.0275959	total: 1m 3s	remaining: 9.44s
871:	learn: 0.0275865	total: 1m 3s	remaining: 9.36s
872:	learn: 0.0275486	total: 1m 3s	remaining: 9.29s
873:	learn: 0.0275251	total: 1m 3s	remaining: 9.21s
874:	learn: 0.0275177	total: 1m 3s	remaining: 9.14s
875:	learn: 0.0274931	total: 1m 4s	remaining: 9.07s
876:	learn: 0.0274728	total: 1m 4s	remaining: 8.99s
877:	learn: 0.0274467	total: 1m 4s	remaining: 8.92s
878:	learn: 0.0274305	total: 1m 4s	remaining: 8.85s
879:	learn: 0.0274257	total: 1m 4s	remaining: 8.77s
880:	learn: 0.0274160	total: 1m 4s	remaining: 8.7s
881:	learn: 0.0273662	total: 1m 4s	remaining: 8.63s
882:	learn: 0.0273267	total: 1m 4s	remaining: 8.55s
883:	learn: 0.0272886	total: 1m 4s	remaining: 8.48s
884:	learn: 0.0272721	total: 1m 4s	remaining: 8.41s
885:	learn: 0.0272600	total: 1m 4s	remaining: 8.33s
886:	learn: 0.0272470	total: 1m 4s	remaining: 8.26s
887:	learn: 0.0272262	total: 1m 4s	remaining: 8.19s
888:	learn: 0.0271927	total: 1m 5s	remaining: 8.12s
889:	learn: 0.0271558	total: 1m 5s	remaining: 8.04s
890:	learn: 0.0271322	total: 1m 5s	remaining: 7.97s
891:	learn: 0.0271149	total: 1m 5s	remaining: 7.9s
892:	learn: 0.0270891	total: 1m 5s	remaining: 7.83s
893:	learn: 0.0270694	total: 1m 5s	remaining: 7.76s
894:	learn: 0.0270256	total: 1m 5s	remaining: 7.68s
895:	learn: 0.0269944	total: 1m 5s	remaining: 7.61s
896:	learn: 0.0269768	total: 1m 5s	remaining: 7.54s
897:	learn: 0.0269207	total: 1m 5s	remaining: 7.47s
898:	learn: 0.0269147	total: 1m 5s	remaining: 7.39s
899:	learn: 0.0268989	total: 1m 5s	remaining: 7.32s
900:	learn: 0.0268837	total: 1m 5s	remaining: 7.25s
901:	learn: 0.0268706	total: 1m 6s	remaining: 7.17s
902:	learn: 0.0268657	total: 1m 6s	remaining: 7.1s
903:	learn: 0.0268493	total: 1m 6s	remaining: 7.02s
904:	learn: 0.0268270	total: 1m 6s	remaining: 6.95s
905:	learn: 0.0267946	total: 1m 6s	remaining: 6.88s
906:	learn: 0.0267800	total: 1m 6s	remaining: 6.8s
907:	learn: 0.0267778	total: 1m 6s	remaining: 6.73s
908:	learn: 0.0267504	total: 1m 6s	remaining: 6.66s
909:	learn: 0.0266995	total: 1m 6s	remaining: 6.58s
910:	learn: 0.0266885	total: 1m 6s	remaining: 6.51s
911:	learn: 0.0266761	total: 1m 6s	remaining: 6.44s
912:	learn: 0.0266690	total: 1m 6s	remaining: 6.36s
913:	learn: 0.0266603	total: 1m 6s	remaining: 6.29s
914:	learn: 0.0266397	total: 1m 6s	remaining: 6.22s
915:	learn: 0.0266114	total: 1m 7s	remaining: 6.14s
916:	learn: 0.0265894	total: 1m 7s	remaining: 6.07s
917:	learn: 0.0265773	total: 1m 7s	remaining: 6s
918:	learn: 0.0265516	total: 1m 7s	remaining: 5.92s
919:	learn: 0.0265224	total: 1m 7s	remaining: 5.85s
920:	learn: 0.0264956	total: 1m 7s	remaining: 5.78s
921:	learn: 0.0264781	total: 1m 7s	remaining: 5.71s
922:	learn: 0.0264666	total: 1m 7s	remaining: 5.63s
923:	learn: 0.0264438	total: 1m 7s	remaining: 5.56s
924:	learn: 0.0264289	total: 1m 7s	remaining: 5.49s
925:	learn: 0.0264112	total: 1m 7s	remaining: 5.41s
926:	learn: 0.0264037	total: 1m 7s	remaining: 5.34s
927:	learn: 0.0263710	total: 1m 7s	remaining: 5.27s
928:	learn: 0.0263151	total: 1m 8s	remaining: 5.2s
929:	learn: 0.0262954	total: 1m 8s	remaining: 5.12s
930:	learn: 0.0262685	total: 1m 8s	remaining: 5.05s
931:	learn: 0.0262543	total: 1m 8s	remaining: 4.98s
932:	learn: 0.0262536	total: 1m 8s	remaining: 4.9s
933:	learn: 0.0262512	total: 1m 8s	remaining: 4.83s
934:	learn: 0.0262166	total: 1m 8s	remaining: 4.76s
935:	learn: 0.0261868	total: 1m 8s	remaining: 4.68s
936:	learn: 0.0261719	total: 1m 8s	remaining: 4.61s
937:	learn: 0.0261061	total: 1m 8s	remaining: 4.54s
938:	learn: 0.0260817	total: 1m 8s	remaining: 4.46s
939:	learn: 0.0260315	total: 1m 8s	remaining: 4.39s
940:	learn: 0.0259928	total: 1m 8s	remaining: 4.32s
941:	learn: 0.0259708	total: 1m 8s	remaining: 4.25s
942:	learn: 0.0259407	total: 1m 9s	remaining: 4.17s
943:	learn: 0.0259219	total: 1m 9s	remaining: 4.1s
944:	learn: 0.0259068	total: 1m 9s	remaining: 4.03s
945:	learn: 0.0258869	total: 1m 9s	remaining: 3.95s
946:	learn: 0.0258849	total: 1m 9s	remaining: 3.88s
947:	learn: 0.0258766	total: 1m 9s	remaining: 3.81s
948:	learn: 0.0258540	total: 1m 9s	remaining: 3.73s
949:	learn: 0.0258341	total: 1m 9s	remaining: 3.66s
950:	learn: 0.0258197	total: 1m 9s	remaining: 3.59s
951:	learn: 0.0257988	total: 1m 9s	remaining: 3.51s
952:	learn: 0.0257935	total: 1m 9s	remaining: 3.44s
953:	learn: 0.0257525	total: 1m 9s	remaining: 3.37s
954:	learn: 0.0257325	total: 1m 9s	remaining: 3.29s
955:	learn: 0.0257001	total: 1m 10s	remaining: 3.22s
956:	learn: 0.0256667	total: 1m 10s	remaining: 3.15s
957:	learn: 0.0256455	total: 1m 10s	remaining: 3.08s
958:	learn: 0.0256300	total: 1m 10s	remaining: 3s
959:	learn: 0.0256006	total: 1m 10s	remaining: 2.93s
960:	learn: 0.0255832	total: 1m 10s	remaining: 2.86s
961:	learn: 0.0255518	total: 1m 10s	remaining: 2.78s
962:	learn: 0.0255307	total: 1m 10s	remaining: 2.71s
963:	learn: 0.0255047	total: 1m 10s	remaining: 2.64s
964:	learn: 0.0254848	total: 1m 10s	remaining: 2.56s
965:	learn: 0.0254769	total: 1m 10s	remaining: 2.49s
966:	learn: 0.0254711	total: 1m 10s	remaining: 2.42s
967:	learn: 0.0254410	total: 1m 10s	remaining: 2.34s
968:	learn: 0.0254133	total: 1m 10s	remaining: 2.27s
969:	learn: 0.0254010	total: 1m 11s	remaining: 2.2s
970:	learn: 0.0253750	total: 1m 11s	remaining: 2.12s
971:	learn: 0.0253623	total: 1m 11s	remaining: 2.05s
972:	learn: 0.0253394	total: 1m 11s	remaining: 1.98s
973:	learn: 0.0253138	total: 1m 11s	remaining: 1.9s
974:	learn: 0.0252899	total: 1m 11s	remaining: 1.83s
975:	learn: 0.0252742	total: 1m 11s	remaining: 1.76s
976:	learn: 0.0252615	total: 1m 11s	remaining: 1.68s
977:	learn: 0.0252538	total: 1m 11s	remaining: 1.61s
978:	learn: 0.0252461	total: 1m 11s	remaining: 1.54s
979:	learn: 0.0252435	total: 1m 11s	remaining: 1.46s
980:	learn: 0.0252268	total: 1m 11s	remaining: 1.39s
981:	learn: 0.0252214	total: 1m 11s	remaining: 1.32s
982:	learn: 0.0252120	total: 1m 11s	remaining: 1.24s
983:	learn: 0.0251694	total: 1m 12s	remaining: 1.17s
984:	learn: 0.0251372	total: 1m 12s	remaining: 1.1s
985:	learn: 0.0251207	total: 1m 12s	remaining: 1.02s
986:	learn: 0.0250923	total: 1m 12s	remaining: 952ms
987:	learn: 0.0250871	total: 1m 12s	remaining: 879ms
988:	learn: 0.0250453	total: 1m 12s	remaining: 806ms
989:	learn: 0.0250268	total: 1m 12s	remaining: 732ms
990:	learn: 0.0250053	total: 1m 12s	remaining: 659ms
991:	learn: 0.0249734	total: 1m 12s	remaining: 586ms
992:	learn: 0.0249501	total: 1m 12s	remaining: 513ms
993:	learn: 0.0249169	total: 1m 12s	remaining: 440ms
994:	learn: 0.0249088	total: 1m 12s	remaining: 366ms
995:	learn: 0.0249062	total: 1m 12s	remaining: 293ms
996:	learn: 0.0249034	total: 1m 13s	remaining: 220ms
997:	learn: 0.0248795	total: 1m 13s	remaining: 147ms
998:	learn: 0.0248699	total: 1m 13s	remaining: 73.3ms
999:	learn: 0.0248468	total: 1m 13s	remaining: 0us
CatBoostClassifier
Time taken : 386.49 secs
Best score : 0.7335
****************************************
Training LGBMClassifier :
LGBMClassifier
Time taken : 70.06 secs
Best score : 0.7087
****************************************

Hyperparameter Optimzation

In [0]:
# intermediate model
# The XGBoost model gave the best base score therefore it was tuned to beat the benchmark score.
# submitting model to kaggle. Multiple parameters were trained using some help from the following links.
# https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e

%%time
# Due to technical limitations, I ultimately ended up using randomizedsearch instead of gridsearch here. 
model = xgb.XGBClassifier(objective= 'binary:logistic')

param_grid = {'subsample' : [0.8,1],
          'n_estimators' : [100, 1000], 
          'learning_rate' : [0.01,0.001],   
          'max_depth': [4,5,6,7],
          'scale_pos_weight': [10,25],
          'gamma': [1,5,10],
          'colsample_bytree' : [0.3,0.5,0.8]
              }


kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
random_search = RandomizedSearchCV(model,param_distributions=param_grid,n_iter=50,scoring='roc_auc',n_jobs=-1,cv=kfold,verbose=1)
#grid_search = GridSearchCV(model, param_grid = param_grid, scoring="roc_auc", n_jobs=-1, cv=kfold, verbose=1)
grid_result = random_search.fit(X_cleaned, y)

print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_)) 
Fitting 5 folds for each of 50 candidates, totalling 250 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed: 46.6min
/usr/local/lib/python3.6/dist-packages/joblib/externals/loky/process_executor.py:706: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.
  "timeout or by a memory leak.", UserWarning
[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed: 175.5min
[Parallel(n_jobs=-1)]: Done 250 out of 250 | elapsed: 242.1min finished
Best: 0.766841 using {'subsample': 0.8, 'scale_pos_weight': 10, 'n_estimators': 100, 'max_depth': 7, 'learning_rate': 0.01, 'gamma': 5, 'colsample_bytree': 0.8}
CPU times: user 1min 13s, sys: 710 ms, total: 1min 14s
Wall time: 4h 3min 14s
In [0]:
# final model
# The XGBoost model gave the best base score therefore it was tuned to beat the benchmark score.
# submitting model to kaggle. Multiple parameters were trained using some help from the following links.
# https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e

%%time

model = xgb.XGBClassifier(objective= 'binary:logistic')

param_grid = {'n_estimators' : [20, 50, 100], 
          'learning_rate' : [0.01,0.001],   
          'max_depth': [4,5,6,7],
          'scale_pos_weight': [1,10],
          'colsample_bytree' : [0.5,0.8]
              }


kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
grid_search = GridSearchCV(model, param_grid = param_grid, scoring="roc_auc", n_jobs=-1, cv=kfold, verbose=1)
grid_result = grid_search.fit(X_cleaned, y)

print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_)) 
Fitting 5 folds for each of 96 candidates, totalling 480 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:  2.9min
[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed: 15.8min
[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed: 45.0min
[Parallel(n_jobs=-1)]: Done 480 out of 480 | elapsed: 51.1min finished
Best: 0.766173 using {'colsample_bytree': 0.5, 'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 100, 'scale_pos_weight': 10}
CPU times: user 31.5 s, sys: 705 ms, total: 32.2 s
Wall time: 51min 33s
In [0]:
# submitted after the model above but gave worse score.
# The XGBoost model gave the best base score therefore it was tuned to beat the benchmark score.
# submitting model to kaggle. Multiple parameters were trained using some help from the following links.
# https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e
# this time with 10 splits.
%%time

model = xgb.XGBClassifier(objective= 'binary:logistic')

param_grid = {'n_estimators' : [20, 50, 100], 
          'learning_rate' : [0.01,0.001],   
          'max_depth': [4,5,6,7],
          'scale_pos_weight': [1,10],
          'colsample_bytree' : [0.5,0.8]
              }


kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)
grid_search = GridSearchCV(model, param_grid = param_grid, scoring="roc_auc", n_jobs=-1, cv=kfold, verbose=1)
grid_result = grid_search.fit(X_cleaned, y)

print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_)) 
Fitting 10 folds for each of 96 candidates, totalling 960 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:  2.6min
[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed: 16.9min
[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed: 40.1min
[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed: 89.0min
[Parallel(n_jobs=-1)]: Done 960 out of 960 | elapsed: 115.1min finished
Best: 0.770858 using {'colsample_bytree': 0.8, 'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 100, 'scale_pos_weight': 10}
CPU times: user 47.6 s, sys: 2.03 s, total: 49.7 s
Wall time: 1h 55min 46s

Part 3: Kaggle Competition

In [0]:
## best params
opt_params = {'colsample_bytree': 0.5, 'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 100, 'scale_pos_weight': 10}
model = xgb.XGBClassifier(**opt_params)
model.fit(X_cleaned,y)
Out[0]:
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=0.5, gamma=0,
              learning_rate=0.01, max_delta_step=0, max_depth=5,
              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,
              nthread=None, objective='binary:logistic', random_state=0,
              reg_alpha=0, reg_lambda=1, scale_pos_weight=10, seed=None,
              silent=None, subsample=1, verbosity=1)
In [0]:
test_pred = model.predict_proba(mailout_test_cleaned)
In [0]:
submission = pd.DataFrame({'LNR':mailout_test_cleaned.index, 'RESPONSE':test_pred[:, 1]})
In [0]:
%cd /content/gdrive/My Drive/Bertelsmann Data Science Project
submission.to_csv('submission_again.csv', index=False)
/content/gdrive/My Drive/Bertelsmann Data Science Project

The best score I got on leaderboard from training different models is 0.80453 which beats the benchmark score of 0.79772 and puts me in 22th position in the competition's public leaderboard at the time of writing.